27 citations · 29 across the 10 of their papers we have counts for
10 papers
Robust Failure, Conservative Repair: Textual Knowledge Distillation from Cross-Model Failures
Andrew Ren, Haokun Liu, Chenhao Tan
Failure-based textual knowledge distillation aims to discover gaps in a model's knowledge by examining its task errors. The distilled knowledge can be useful for the reasoning of b…
Emergent Misalignment Is Not Magical
Mingxuan Li, Qirun Dai, Heran Wang +1
Fine-tuning large language models (LLMs) on narrowly harmful datasets can lead to misalignment broadly, a phenomenon known as emergent misalignment (EM). EM poses a challenge for A…
The Interaction Tax: When Communication Erases Diversity in Multi-Agent Teams
Summer Eunhyung Ann, Haokun Liu, Chenhao Tan
Does multi-agent LLM interaction help or hurt? Some work reports gains from debate (Du et al., 2024), critique loops (Chen et al., 2025), and mixture-of-agents synthesis (Wang et a…
VERITAS: Towards a General-Purpose Replication Tool for Scientific Research
Haokun Liu, Filbert Aurelian Tjiaranata, Chenhao Tan
AI tools are accelerating scientific publication while the systems that review it struggle to keep up, and independent verification of published research has become both harder and…
Personalized Benchmarking: Evaluating LLMs by Individual Preferences
Cristina Garbacea, Heran Wang, Chenhao Tan
With the rise in capabilities of large language models (LLMs) and their deployment in real-world tasks, evaluating LLM alignment with human preferences has become an important chal…
Automatically Generating Hard Math Problems from Hypothesis-Driven Error Analysis
Jiayu Fu, Mourad Heddaya, Chenhao Tan
Numerous math benchmarks exist to evaluate LLMs' mathematical capabilities. However, most involve extensive manual effort and are difficult to scale. Consequently, they cannot keep…
HypoEval: Hypothesis-Guided Evaluation for Natural Language Generation
Mingxuan Li, Hanchen Li, Chenhao Tan
Large language models (LLMs) have demonstrated great potential for automating the evaluation of natural language generation. Previous frameworks of LLM-as-a-judge fall short in two…
HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation
Haokun Liu, Sicong Huang, Jingyu Hu +2
There is growing interest in hypothesis generation with large language models (LLMs). However, fundamental questions remain: what makes a good hypothesis, and how can we systematic…
Literature Meets Data: A Synergistic Approach to Hypothesis Generation
Haokun Liu, Yangqiaoyu Zhou, Mingxuan Li +2
AI holds promise for transforming scientific processes, including hypothesis generation. Prior work on hypothesis generation can be broadly categorized into theory-driven and data-…
Hypothesis Generation with Large Language Models
Yangqiaoyu Zhou, Haokun Liu, Tejes Srivastava +2
Effective generation of novel hypotheses is instrumental to scientific progress. So far, researchers have been the main powerhouse behind hypothesis generation by painstaking data…