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Think Through Uncertainty: Improving Long-Form Generation Factuality via Reasoning Calibration
Xin Liu, Lu Wang
Large language models (LLMs) often hallucinate in long-form generation. Existing approaches mainly improve factuality through post-hoc revision or reinforcement learning (RL) with…
Logit Arithmetic Elicits Long Reasoning Capabilities Without Training
Yunxiang Zhang, Muhammad Khalifa, Lechen Zhang +5
Large reasoning models exhibit long chain-of-thought reasoning with complex strategies such as backtracking and self-verification. Yet, these capabilities typically require resourc…
Logit Arithmetic Elicits Long Reasoning Capabilities Without Training
Yunxiang Zhang, Muhammad Khalifa, Lechen Zhang +5
Large reasoning models (LRMs) can do complex reasoning via long chain-of-thought (CoT) involving cognitive strategies such as backtracking and self-correction. Recent studies sugge…
Answer Convergence as a Signal for Early Stopping in Reasoning
Xin Liu, Lu Wang
Chain-of-thought (CoT) prompting enhances reasoning in large language models (LLMs) but often leads to verbose and redundant outputs, thus increasing inference cost. We hypothesize…
VeriFact: Enhancing Long-Form Factuality Evaluation with Refined Fact Extraction and Reference Facts
Xin Liu, Lechen Zhang, Sheza Munir +2
Large language models (LLMs) excel at generating long-form responses, but evaluating their factuality remains challenging due to complex inter-sentence dependencies within the gene…
FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation
Farima Fatahi Bayat, Lechen Zhang, Sheza Munir +1
The rapid adoption of language models (LMs) across diverse applications has raised concerns about their factuality, i.e., their consistency with real-world facts. We first present…
Enhanced Language Model Truthfulness with Learnable Intervention and Uncertainty Expression
Farima Fatahi Bayat, Xin Liu, H. V. Jagadish +1
Large language models (LLMs) can generate long-form and coherent text, yet they often hallucinate facts, which undermines their reliability. To mitigate this issue, inference-time…
Enhancing Language Model Factuality via Activation-Based Confidence Calibration and Guided Decoding
Xin Liu, Farima Fatahi Bayat, Lu Wang
Calibrating language models (LMs) aligns their generation confidence with the actual likelihood of answer correctness, which can inform users about LMs' reliability and mitigate ha…