diff --git a/Persuasion-Examples.md b/Persuasion-Examples.md new file mode 100644 index 0000000..e68bdd7 --- /dev/null +++ b/Persuasion-Examples.md @@ -0,0 +1,55 @@ +Von Frisch’s work with the waggle dances of honeybees represents one of the first empirically validated examples of non-mammals using sophisticated non-verbal communication where dance movements are symbolically interpreted as a descriptive “language” that uses an iconic relationship to the geographic location of a food source. + +broken wing displays performed by adult birds of various genera, which create the false impression for a nearby predator that the bird is injured, and thus an easy target. This allows the bird to lead the predator away from vulnerable chicks (Griffin, 2001, p. 222). + + +In the case of chimpanzees in one facility where all of the chimps were taught American Sign Language, two new chimps were brought in, and through no human prompting, were taught sign language by the existing community (Fouts et al., 2002). This second generation of chimps became so reliant on the sign language that they would even use it during periods of high tension or aggravation, like when fighting or engaging in rowdy play, when one would expect them to resort to more “natural” chimpanzee forms of communication (Fouts et al., 2002, p. 288) + + + + +George Kennedy, who was among the first to revive interest in +nonhuman animal communication among rhetoricians, famously describes the various stages +of a red deer fight in order to highlight the possibilities of analogy when studying persuasive +communication across species lines (Kennedy, 1998, pp. 13–14). +So how do the minds of red deer stags in rut operate? At first the stags will adopt a low-stakes +communicative behavior – they will simply bugle at one another in an attempt to run off lesser +competitors for the does. If several rounds of these vocal calls do not work, the bucks enter a +series of motions where they turn and walk at right angles to one another, then turn and strut +again at another right angle, repeating this process several times as if they were “displaying the +goods” to their potential opponents. Perhaps the thinking is that if one stag is obviously larger +and more muscular it will be easier to scare off other suitors. It is only after these attempts at +persuasive communication have failed, which happens less than a third of the time, that either +deer will initiate a physical conflict. We can thus infer that these display rituals are embraced +by most members of a herd because they are so beneficial. Kennedy provided this example +because it illustrates the urge to communicate in order to fulfill two of the aforementioned +desires that drive all species of animal for a significant portion of their lives – to secure sex +and to preserve bodily health (through avoiding costly physical encounters by means of less +costly verbal and visual rhetoric). + + + +– humans in the corporate world call them team +bonding exercises – through ritual and collective activity (Parrish, 2013, pp. 40–41). In what +they refer to as a “greeting ceremony,” Maynard Smith and Harper describe a common ritual +where African wild dogs gather to vocalize together, sound off as if in roll call, and initiate +play fights together, as Olympic wrestlers would do to warm up before a match (Maynard +Smith & Harper, 2003, p. 127). Making such boisterous displays could be a liability because +of the potential to alert large predators to the location of vulnerable dogs, but the epideictic +rituals pay off in unifying and preparing the pack for the demanding trials of endurance predation +(sometimes called persistence hunting). African wild dogs are not the fastest, strongest, +or largest species on the savannah, so much like early hominins they will often wear out their +prey by chasing them at low speeds over long distances. Whereas a human persistence hunter +can outdistance most prey animals that are better at short bursts of speed, the dogs need help +from other members of their pack. So they run a relay race of sorts, only they haven’t informed +their competitor ahead of time that the rules allow for a passing of the baton, so to speak. A +small number of dogs will identify and flush out a likely target, slowly herding it toward the +position of the next dogs down the line in a coordinated cross-country course. Once the prey +reaches the next dogs’ positions, they take over while the first group catches its breath. This +process of chasing and handing off the prey can go on for quite a few iterations, until the poor +animal is worn out and one member of the pack is able to hamstring it or otherwise cripple +the beast. Then the refreshed members of the pack will calmly trot in and help the victorious +group finish their prey in what is sadly not often the most merciful of deaths. + + + diff --git a/awesome-lists/memorability.md b/awesome-lists/memorability.md new file mode 100644 index 0000000..6424f98 --- /dev/null +++ b/awesome-lists/memorability.md @@ -0,0 +1,95 @@ +# Awesome Media Memorability [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) +🤩 An AWESOME Curated List of Papers, Workshops, Datasets, and Challenges in Media Memorability + +## Contents +- [Memorable Media Generation](#memorable-media-generation) +- [Media Memorability Prediction](#media-memorability-prediction) + +--- + +## Papers + +### Memorable Media Generation +- [**Long-Term Ad Memorability: Understanding & Generating Memorable Ads**](https://arxiv.org/abs/2309.00378) + + *Harini S I, Somesh Singh, Yaman K Singla, Aanisha Bhattacharyya, Veeky Baths, Changyou Chen, Rajiv Ratn Shah, Balaji Krishnamurthy* (WACV 2025) + + **Modality** Multimodal, Video, Speech, Image + +- [**How to Make an Image More Memorable? A Deep Style Transfer Approach**](https://dl.acm.org/doi/10.1145/3078971.3078986) + + *Aliaksandr Siarohin, Gloria Zen, Cveta Majtanovic, et al.* (ICMR, 2017) + + **Modality**: Image + +### Media Memorability Prediction + +- [**Long-Term Ad Memorability: Understanding & Generating Memorable Ads**](https://arxiv.org/abs/2309.00378) + + *Harini S I, Somesh Singh, Yaman K Singla, Aanisha Bhattacharyya, Veeky Baths, Changyou Chen, Rajiv Ratn Shah, Balaji Krishnamurthy* (WACV 2025) + + **Modality** Multimodal, Video, Speech, Image + +- [**Modular Memorability: Tiered Representations for Video Memorability Prediction**](https://openaccess.thecvf.com/content/CVPR2023/html/Dumont_Modular_Memorability_Tiered_Representations_for_Video_Memorability_Prediction_CVPR_2023_paper.html) + + *Théo Dumont, Juan Segundo Hevia, Camilo L. Fosco.* (CVPR, 2023) + + **Modality**: Video + +- [**Overview of the MediaEval 2022 Predicting Video Memorability Task**](https://ceur-ws.org/Vol-3583/paper17.pdf) + + *Lorin Sweeney, Mihai Gabriel Constantin, Claire-Hélène Demarty, et al.* (MediaEval, 2022) + + **Modality**: Video + +- [**Image Memorability Prediction Using Depth and Motion Cues**](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9007618) + + *Sathisha Basavaraju, Arijit Sur.* (IEEE Transactions on Computational Social Systems, 2020) + + **Modality**: Image + +- [**Videomem: Constructing, Analyzing, Predicting Short-Term and Long-Term Video Memorability**](https://openaccess.thecvf.com/content_ICCV_2019/papers/Cohendet_VideoMem_Constructing_Analyzing_Predicting_Short-Term_and_Long-Term_Video_Memorability_ICCV_2019_paper.pdf) + + *Romain Cohendet, Claire-Hélène Demarty, Ngoc QK Duong, Martin Engilberge.* (ICCV, 2019) + + **Modality**: Video + +- [**Defining Image Memorability Using the Visual Memory Schema**](https://pubmed.ncbi.nlm.nih.gov/31056491/) + + *Erdem Akagunduz, Adrian G Bors, Karla K Evans.* (TPAMI, 2019) + + **Modality**: Image + +- [**Show and Recall: Learning What Makes Videos Memorable**](https://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w40/Shekhar_Show_and_Recall_ICCV_2017_paper.pdf) + + *Sumit Shekhar, Dhruv Singal, Harvineet Singh, et al.* (ICCV Workshops, 2017) + + **Modality**: Video + +- [**Learning Computational Models of Video Memorability from fMRI Brain Imaging**](https://pubmed.ncbi.nlm.nih.gov/25314715/) + + *Junwei Han, Changyuan Chen, et al.* (Transactions on Cybernetics, 2014) + + **Modality**: Video + +- [**Relative Spatial Features for Image Memorability**](https://dl.acm.org/doi/10.1145/2502081.2502198) + + *Jongpil Kim, Sejong Yoon, Vladimir Pavlovic.* (ACM MM, 2013) + + **Modality**: Image + +- [**What Makes a Photograph Memorable?**](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6629991) + + *Phillip Isola, Jianxiong Xiao, Antonio Torralba, Aude Oliva.* (TPAMI, 2013) + + **Modality**: Image + +- [**The Intrinsic Memorability of Face Photographs**](https://pubmed.ncbi.nlm.nih.gov/24246059/) + *Wilma A Bainbridge, Phillip Isola, Aude Oliva.* (Journal of Experimental Psychology: General, 2013) + + **Modality**: Image + +## Contributing + +We welcome contributions! Please follow the [contribution guidelines](CONTRIBUTING.md) to submit new findings to media memorability. +You can create a PR to add/modify/delete your entry, open to suggesntions \ No newline at end of file diff --git a/index.html b/index.html index 4151342..39b8f3c 100644 --- a/index.html +++ b/index.html @@ -54,6 +54,7 @@

The Behavior Modality: Studying Human Behavior In The Wild

  • Measuring And Improving Persuasiveness Of Generative Models
  • Align Via Actions : Learning Behavior Aligns LLMs With Human Opinions in Zero-Shot
  • The Culture Repository
    • +
  • The Behavior Modality
    • diff --git a/measure-engagement.html b/measure-engagement.html new file mode 100644 index 0000000..d523866 --- /dev/null +++ b/measure-engagement.html @@ -0,0 +1,595 @@ + + + + + + + + + Measuring And Improving Persuasiveness Of Large Language Models + + + + + + + + + + + + + + + + + +
    + +
    + +
    +
    +
    +
    +
    +

    Measuring And Improving Persuasiveness Of Large Language Models

    +
    + + Somesh Singh*, + + + Yaman Kumar*, + + + S I Harini*, + + + Balaji Krishnamurthy, + + +
    + Adobe LogoMedia and Data Science Research (MDSR), Adobe +
    + +
    + *Equal Contribution +
    + +

    Get in touch with us at behavior-in-the-wild@googlegroups.com

    + +
    +
    + + + + + + Access Paper + + + + + + + + Code + + + + + + + + Arena + + + + + + + + Dataset + + + + + + + + Experiment + + + + + + + + Examples + + +
    +
    +
    +
    +
    +
    +
    + +
    +
    +
    +

    + 🔥[NEW!]Introducing PersuasionBench and PersuasionArena - First large-scale automated benchmark and arena to measure the persuasive abilities of large language models. +
    + 🔥[NEW!]We introduce the task of transsuasion, the task of transferring content from one behavior to another while holding the other conditions like meaning, speaker, and time constant. +
    + 🔥[NEW!] Challenging Scale Assumptions - Smaller models can outperform larger ones in persuasion when trained on targeted datasets. +
    + 🔥[NEW!]Policy Implications - Current regulations like SB-1047 and EU AI law fail to capture the full impact of AI on society, highlighting the need for more comprehensive measures. +
    + 🔥[NEW!]We release the Persuasion Leaderboard and you can also participate in the persuasion Human-Eval +

    +
    +
    +
    + +
    +
    +
    +
    +

    Persuasion Leaderboard

    +

    Here are the results of our models on the Persuasion Leaderboard. The leaderboard is based on the paper and the PersuasionArena website.

    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ModelAvg. Elo
    Topline (T2) 🥇1357
    Ours (13B) 🥈1293
    Ours-Instruct (13B) 🥉1304
    Ours (CS+BS) (13B)1299
    Vicuna-1.5-13B1195
    LLaMA3-70B1099
    GPT-3.5877
    GPT-4o1187
    GPT-41092
    Baseline (T1)1251
    GPT-41213
    Baseline (T1)979
    + +
    + +
    + +
    +
    +

    Transsuasion Examples

    + + A few samples showing Transsuasion. While the account, time, and meaning of the samples remain similar, the behavior over the samples varies significantly. + A few samples showing Transsuasion. While the account, time, and meaning of the samples remain similar, the behavior over the samples varies significantly. +

    + A few samples showing Transsuasion. While the account, time, and meaning of the samples remain similar, the behavior over the samples varies significantly. + +

    + + +A few samples showing Transsuasion using our model. The left part contains original low-liked tweet, and the right contains the transsuaded version of the tweet. +A few samples showing Transsuasion using our model. The left part contains original low-liked tweet, and the right contains the transsuaded version of the tweet. +

    +A few samples showing Transsuasion using our model. The left part contains original low-liked tweet, and the right contains the transsuaded version of the tweet. +

    + +
    +
    + + + + + +
    +
    +
    +
    +

    Abstract

    +
    +

    + Crafting a message to elicit a desired response can be time-consuming. While prior research has explored content generation and popularity prediction, the impact of wording on behavior change has been underexplored. We introduce the concept of transsuasion (trans = carrying across, suasion = the act of persuading, transsuasion = the act of carrying across text from non-persuasive to persuasive). +

      +
    1. Data Generation. We utilize pairs of tweets by the same user with similar meanings but varying wording and likes to study transsuasion.
      2. +
    2. LLM Persuasion. Our research shows that larger language models (LLMs) are more effective at identifying which tweet versions attract more likes and can transform low-performing versions into high-performing ones.
      3. +
    3. Model Efficiency. We demonstrate that smaller LLMs can be optimized to surpass larger LLMs in persuasion abilities.
      4. +
    4. Resources. We introduce PersuasionBench and PersuasionArena, providing a benchmark and a suite of tasks for evaluating and enhancing persuasion in text. Our benchmarks and models are publicly available.
      5. +
    +

    + +
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    + +

    + + +
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    Transsuasion Data

    +
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    + We are releasing our test dataset in the huggingface format [HuggingFace Dataset]. + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    CaseUsernameMedia FilterLink MatchTextEditLikes %InputOutput#Samples
    Refine text (Ref)SameNo ImagesNo>0.8-40T1T2265k
    Paraphrase (Parap)SameNo ImagesNo>0.6>0.640T1T2163K
    Transsuade and Add Image (AddImg)
    		Same
    Image only on o/p side
    		No>0.6>0.640T1T2, I248k
    Free-form refine with text and optionally visual content (FFRef)
    		Same
    Image on either or both sides
    		No>0.8-40T1,I1T2,I2701k
    Free-form paraphrase with text and optionally visual content (FFPara)
    		Same
    Image on either or both sides
    		No>0.6>0.640T1,I1T2,I224k
    Transsuade Visual Only (VisOnly)
    		SameImage similarity > 0.7No--40T1,I1,T2I268k
    Transsuade Text Only (TextOnly)
    		Same
    Image on o/p side or both sides
    		No>0.8-40T1,I1,I2T269k
    Highlight Different Aspects of Context (Hilight)
    		SameImages IgnoredYes>0.6>0.640T1,Con1,I1T2,I2241k
    Transcreation (Transc)
    		BrandImages IgnoredIgnored>0.8>-20T1,U1I1T2,U2I2131k
    +
    + + + + + + + + + + +
    + +
    +
    +
    +

    Human Eval

    +

    To participate in the Human Eval fill the following form

    +

    You need to accept the Terms of Service beforehand

    +
    +
    +
    + + + + + + + + + + + + + + + + + + +
    + +
    +
    +

    BibTeX

    + 
    
+        @article{singh2024measuring,
+          title={Measuring and Improving Persuasiveness of Large Language Models},
+          author={Somesh Singh and Yaman K Singla and Harini SI and Balaji Krishnamurthy},
+          year={2024},
+          journal={arXiv preprint arXiv:2410.02653}
+      }
+
    +
    + +
    + +
    +
    +

    Terms Of Service

    +

    + Users are required to agree to the following terms before using the service +
    The service is a research preview. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. Please do not upload any private information. The service collects user dialogue data, including both text and images, and reserves the right to distribute it under a Creative Commons Attribution (CC-BY) or a similar license. +
    They are also restricted to uses that follow the license agreement of Twitter, and LLaMA +

    +
    +
    + + +
    +
    +

    Acknowledgement

    +

    + We thank Adobe for their generous sponsorship. +
    + We thank the LLaMA team for giving us access to their models, and open-source projects, including Vicuna. +
    +

    +

    +

    +
    +
    + + + + + \ No newline at end of file diff --git a/persuasion-blog.html b/persuasion-blog.html new file mode 100644 index 0000000..ee34a48 --- /dev/null +++ b/persuasion-blog.html @@ -0,0 +1,130 @@ + + + + + + + + + Measuring And Improving Persuasiveness Of Generative Models + + + + + + + + + + + + + + + + + +
    + +
    + +
    +
    +
    +
    +
    +

    How Persuasive are LLMs Exactly? Can They Be Made More Persuasive?

    +

    The Opportunities and Risks of Automated Persuasion

    +
    + + Yaman Kumar Singla + + + +
    + Research Scientist,
    Adobe LogoAdobe Media and Data Science Research (MDSR) +
    + +

    ✉️ behavior-in-the-wild@googlegroups.com

    + +
    +
    + + + + + + Access Paper + + +
    +
    +
    +
    +
    +
    +
    + + + +
    +
    +
    +
    +
    +

    + Abstract: Large language models (LLMs) have shown remarkable capabilities in generating human-like text. However, the persuasiveness of the generated text remains largely unexplored. In this work, we propose a novel framework to measure the persuasiveness of LLMs. We show that LLMs can be used to generate persuasive text, and that the persuasiveness of the generated text can be measured using human evaluations + and automated metrics. We find that LLMs can generate text that is as persuasive as human-written text, and that the persuasiveness of the generated text can be improved by fine-tuning the LLMs on a small amount of labeled data. Our work highlights the opportunities and risks of automated persuasion, and suggests that LLMs can be used to generate persuasive text for a wide range of applications. +

    + +

    Introduction

    +

    + Large language models (LLMs) have shown remarkable capabilities in generating human-like text. However, the persuasiveness of the generated text remains largely unexplored. In this work, we propose a novel framework to measure the persuasiveness of LLMs. We show that LLMs can be used to generate persuasive text, and that the persuasiveness of the generated text can be measured using human evaluations + and automated metrics. We find that LLMs can generate text that is as persuasive as human-written text, and that the persuasiveness of the generated text can be improved by fine-tuning the LLMs on a small amount of labeled data. Our work highlights the opportunities and risks of automated persuasion, and suggests that LLMs can be used to generate persuasive text for a wide range of applications. +

    + +

    Persuasiveness of LLMs

    +

    + We propose a novel framework to measure the persuasiveness of LLMs. We show that LLMs can be used to generate persuasive text, and that the persuasiveness of the generated text can be measured using human evaluations + and automated metrics. We find that LLMs can generate text that is as persuasive as human-written text, and that the persuasiveness of the generated text can be improved by fine-tuning the LLMs on a small amount of labeled data. Our work highlights the opportunities and risks of automated persuasion, and suggests that LLMs can be used to generate persuasive text for a wide range of applications. +

    + +

    Persuasiveness of LLMs

    +

    + We propose a novel framework to measure the persuasiveness of LLMs. We show that LLMs can be used to generate persuasive text, and that the persuasiveness of the generated text can be measured using human evaluations + and automated metrics. We find that LLMs can generate text that is as persuasive as human-written text, and that the persuasiveness of the generated text can be improved by fine-tuning the LLMs on a small amount of labeled data. Our work highlights the opportunities and risks of automated persuasion, and suggests that LLMs can be used to generate persuasive text for a wide range of applications. + +

    + +

    Conclusion

    +

    + We propose a novel framework to measure the persuasiveness of LLMs. We show that LLMs can be used to generate persuasive text, and that the persuasiveness of the generated text can be measured using human evaluations + and automated metrics. We find that LLMs can generate text that is as persuasive as human-written text, and that the persuasiveness of the generated text can be improved by fine-tuning the LLMs on a small amount of labeled data. Our work highlights the opportunities and risks of automated persuasion, and suggests that LLMs can be used to generate persuasive text for a wide range of applications. + +

    +
    +
    +
    +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/teaching-behavior-improves-content-understanding.html b/teaching-behavior-improves-content-understanding.html new file mode 100644 index 0000000..d2bb8ab --- /dev/null +++ b/teaching-behavior-improves-content-understanding.html @@ -0,0 +1,595 @@ + + + + + + + + + Measuring And Improving Persuasiveness Of Large Language Models + + + + + + + + + + + + + + + + + +
    + +
    + +
    +
    +
    +
    +
    +

    Measuring And Improving Persuasiveness Of Large Language Models

    +
    + + Somesh Singh*, + + + Yaman Kumar*, + + + S I Harini*, + + + Balaji Krishnamurthy, + + +
    + Adobe LogoMedia and Data Science Research (MDSR), Adobe +
    + +
    + *Equal Contribution +
    + +

    Get in touch with us at behavior-in-the-wild@googlegroups.com

    + +
    +
    + + + + + + Access Paper + + + + + + + + Code + + + + + + + + Arena + + + + + + + + Dataset + + + + + + + + Experiment + + + + + + + + Examples + + +
    +
    +
    +
    +
    +
    +
    + +
    +
    +
    +

    + 🔥[NEW!]Introducing PersuasionBench and PersuasionArena - First large-scale automated benchmark and arena to measure the persuasive abilities of large language models. +
    + 🔥[NEW!]We introduce the task of transsuasion, the task of transferring content from one behavior to another while holding the other conditions like meaning, speaker, and time constant. +
    + 🔥[NEW!] Challenging Scale Assumptions - Smaller models can outperform larger ones in persuasion when trained on targeted datasets. +
    + 🔥[NEW!]Policy Implications - Current regulations like SB-1047 and EU AI law fail to capture the full impact of AI on society, highlighting the need for more comprehensive measures. +
    + 🔥[NEW!]We release the Persuasion Leaderboard and you can also participate in the persuasion Human-Eval +

    +
    +
    +
    + +
    +
    +
    +
    +

    Persuasion Leaderboard

    +

    Here are the results of our models on the Persuasion Leaderboard. The leaderboard is based on the paper and the PersuasionArena website.

    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ModelAvg. Elo
    Topline (T2) 🥇1357
    Ours (13B) 🥈1293
    Ours-Instruct (13B) 🥉1304
    Ours (CS+BS) (13B)1299
    Vicuna-1.5-13B1195
    LLaMA3-70B1099
    GPT-3.5877
    GPT-4o1187
    GPT-41092
    Baseline (T1)1251
    GPT-41213
    Baseline (T1)979
    + +
    + +
    + +
    +
    +

    Transsuasion Examples

    + + A few samples showing Transsuasion. While the account, time, and meaning of the samples remain similar, the behavior over the samples varies significantly. + A few samples showing Transsuasion. While the account, time, and meaning of the samples remain similar, the behavior over the samples varies significantly. +

    + A few samples showing Transsuasion. While the account, time, and meaning of the samples remain similar, the behavior over the samples varies significantly. + +

    + + +A few samples showing Transsuasion using our model. The left part contains original low-liked tweet, and the right contains the transsuaded version of the tweet. +A few samples showing Transsuasion using our model. The left part contains original low-liked tweet, and the right contains the transsuaded version of the tweet. +

    +A few samples showing Transsuasion using our model. The left part contains original low-liked tweet, and the right contains the transsuaded version of the tweet. +

    + +
    +
    + + + + + +
    +
    +
    +
    +

    Abstract

    +
    +

    + Crafting a message to elicit a desired response can be time-consuming. While prior research has explored content generation and popularity prediction, the impact of wording on behavior change has been underexplored. We introduce the concept of transsuasion (trans = carrying across, suasion = the act of persuading, transsuasion = the act of carrying across text from non-persuasive to persuasive). +

      +
    1. Data Generation. We utilize pairs of tweets by the same user with similar meanings but varying wording and likes to study transsuasion.
      2. +
    2. LLM Persuasion. Our research shows that larger language models (LLMs) are more effective at identifying which tweet versions attract more likes and can transform low-performing versions into high-performing ones.
      3. +
    3. Model Efficiency. We demonstrate that smaller LLMs can be optimized to surpass larger LLMs in persuasion abilities.
      4. +
    4. Resources. We introduce PersuasionBench and PersuasionArena, providing a benchmark and a suite of tasks for evaluating and enhancing persuasion in text. Our benchmarks and models are publicly available.
      5. +
    +

    + +
    +
    +
    +
    +
    + +

    + + +
    +
    +
    +

    Transsuasion Data

    +
    +
    +
    + +
    +
    +
    + We are releasing our test dataset in the huggingface format [HuggingFace Dataset]. + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    CaseUsernameMedia FilterLink MatchTextEditLikes %InputOutput#Samples
    Refine text (Ref)SameNo ImagesNo>0.8-40T1T2265k
    Paraphrase (Parap)SameNo ImagesNo>0.6>0.640T1T2163K
    Transsuade and Add Image (AddImg)
    		Same
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    BibTeX

    + 
    
+        @article{singh2024measuring,
+          title={Measuring and Improving Persuasiveness of Large Language Models},
+          author={Somesh Singh and Yaman K Singla and Harini SI and Balaji Krishnamurthy},
+          year={2024},
+          journal={arXiv preprint arXiv:2410.02653}
+      }
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    Acknowledgement

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    + We thank Adobe for their generous sponsorship. +
    + We thank the LLaMA team for giving us access to their models, and open-source projects, including Vicuna. +
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    + + + + + \ No newline at end of file