2 citations · 4 across the 10 of their papers we have counts for
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Reflective Prompt Tuning through Language Model Function-Calling
Farima Fatahi Bayat, Moin Aminnaseri, Pouya Pezeshkpour +1
Large language models (LLMs) have become increasingly capable of following instructions and complex reasoning, making prompting a flexible interface for adapting models without par…
From Proof to Program: Characterizing Tool-Induced Reasoning Hallucinations in Large Language Models
Farima Fatahi Bayat, Pouya Pezeshkpour, Estevam Hruschka
Tool-augmented Language Models (TaLMs) can invoke external tools to solve problems beyond their parametric capacity. However, it remains unclear whether these tool-enabled gains re…
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…
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…
APE: Active Learning-based Tooling for Finding Informative Few-shot Examples for LLM-based Entity Matching
Kun Qian, Yisi Sang, Farima Fatahi Bayat +11
Prompt engineering is an iterative procedure often requiring extensive manual effort to formulate suitable instructions for effectively directing large language models (LLMs) in sp…
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…
FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge
Farima Fatahi Bayat, Kun Qian, Benjamin Han +6
Detecting factual errors in textual information, whether generated by large language models (LLM) or curated by humans, is crucial for making informed decisions. LLMs' inability to…
CompactIE: Compact Facts in Open Information Extraction
Farima Fatahi Bayat, Nikita Bhutani, H. V. Jagadish
A major drawback of modern neural OpenIE systems and benchmarks is that they prioritize high coverage of information in extractions over compactness of their constituents. This sev…