Papers with Claude-3
Hidden Persuaders: LLMs’ Political Leaning and Their Influence on Voters (2024.emnlp-main)
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| Challenge: | This paper examines the political leanings of large language models (LLMs) in the 2024 election. |
| Approach: | They propose to use large language models to examine users' political leanings in the 2024 presidential election to determine their political preference. |
| Outcome: | The proposed models show that they have a political leaning and can influence political views in the 2024 presidential election. |
AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization via Multi-LLMs (2025.findings-naacl)
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| Challenge: | Existing jailbreak research exhibits limitations in universality, validity, and efficiency . Existing methods for jailbreaking LLMs have limited validity and effectiveness . |
| Approach: | They propose a black-box approach that uses wordplay-guided mapping rule sampling to create universal adversarial prompts. |
| Outcome: | The proposed method efficiently identifies security vulnerabilities across various LLMs, achieving an average success rate of over 80% with fewer than 10 queries. |
xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)
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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Autonomous agents powered by large language models (LLMs) have attracted significant research interest, but there are few standards for developing specialized models for agent tasks. |
| Approach: | They propose a series of large action models with dense and mixture-of-expert architectures that unifies, augments, and synthesizes diverse datasets to enhance agent generalizability and performance. |
| Outcome: | The proposed models outperform GPT-4, Claude-3, and many other models in terms of tool use and outperformed GPT-based models on multiple agent ability benchmarks. |
QAEval: Mixture of Evaluators for Question-Answering Task Evaluation (2025.acl-long)
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| Challenge: | Existing QA evaluation methods struggle with open-ended and unstructured responses. |
| Approach: | They propose a hybrid framework that combines rule-based reliability with LLM-based adaptability to overcome these challenges. |
| Outcome: | The proposed framework outperforms existing models like GPT-4o and Claude-3 in accuracy and cost. |
Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation (2024.emnlp-main)
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| Challenge: | evaluating large language models' output is difficult due to the high cost of human evaluation. |
| Approach: | They propose a family of foundational large autorater models that train on over 100 quality assessment tasks. |
| Outcome: | The proposed model outperforms models on 8 of 12 autorater benchmarks on 53 quality assessment tasks. |
Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter? (2024.emnlp-main)
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Nemika Tyagi, Mihir Parmar, Mohith Kulkarni, Aswin Rrv, Nisarg Patel, Mutsumi Nakamura, Arindam Mitra, Chitta Baral
| Challenge: | Existing studies evaluate only the final predicted answer of a puzzle, without providing any finer metrics to evaluate them. |
| Approach: | They propose to use a grid-based evaluation dataset to evaluate LLMs' reasoning abilities and a new error taxonomy to evaluate their reasoning chains. |
| Outcome: | The proposed model outperforms existing prompting methods on a wide range of natural language understanding tasks previously thought to be exclusive to humans. |