6 citations · 16 across the 15 of their papers we have counts for
11 papers · 1 filter
Hallucinations in LLMs: A Lifecycle-Based Survey of Causes, Detection, Mitigation, and Prevention
Naveen Lamba, Sanju Tiwari, Manas Gaur
The lifecycle of hallucination in LLMs is a concept that enables building solid frameworks on the control and reliability of LLMs in high-stakes environments, including health, leg…
Experiments or Outcomes? Probing Scientific Feasibility in Large Language Models
Seyedali Mohammadi, Manas Gaur, Francis Ferraro
Scientific feasibility assessment asks whether a claim is consistent with established knowledge and whether experimental evidence could support or refute it. We frame feasibility a…
Beyond Memorization: Testing LLM Reasoning on Unseen Theory of Computation Tasks
Shlok Shelat, Jay Raval, Souvik Roy +1
Large language models (LLMs) have demonstrated strong performance on formal language tasks, yet whether this reflects genuine symbolic reasoning or pattern matching on familiar con…
SymLoc: Symbolic Localization of Hallucination across HaluEval and TruthfulQA
Naveen Lamba, Sanju Tiwari, Manas Gaur
LLMs still struggle with hallucination, especially when confronted with symbolic triggers like modifiers, negation, numbers, exceptions, and named entities. Yet, we lack a clear un…
Investigating Symbolic Triggers of Hallucination in Gemma Models Across HaluEval and TruthfulQA
Naveen Lamba, Sanju Tiwari, Manas Gaur
Hallucination in Large Language Models (LLMs) is a well studied problem. However, the properties that make LLM intrinsically vulnerable to hallucinations have not been identified a…
Mental Health Equity in LLMs: Leveraging Multi-Hop Question Answering to Detect Amplified and Silenced Perspectives
Batool Haider, Atmika Gorti, Aman Chadha +1
Large Language Models (LLMs) in mental healthcare risk propagating biases that reinforce stigma and harm marginalized groups. While previous research identified concerning trends,…
Flying Pigs, FaR and Beyond: Evaluating LLM Reasoning in Counterfactual Worlds
Anish R Joishy, Ishwar B Balappanawar, Vamshi Krishna Bonagiri +3
A fundamental challenge in reasoning is navigating hypothetical, counterfactual worlds where logic may conflict with ingrained knowledge. We investigate this frontier for Large Lan…
Unboxing Occupational Bias: Grounded Debiasing of LLMs with U.S. Labor Data
Atmika Gorti, Manas Gaur, Aman Chadha
Large Language Models (LLMs) are prone to inheriting and amplifying societal biases embedded within their training data, potentially reinforcing harmful stereotypes related to gend…
SaGE: Evaluating Moral Consistency in Large Language Models
Vamshi Krishna Bonagiri, Sreeram Vennam, Priyanshul Govil +2
Despite recent advancements showcasing the impressive capabilities of Large Language Models (LLMs) in conversational systems, we show that even state-of-the-art LLMs are morally in…
Measuring Moral Inconsistencies in Large Language Models
Vamshi Krishna Bonagiri, Sreeram Vennam, Manas Gaur +1
A Large Language Model (LLM) is considered consistent if semantically equivalent prompts produce semantically equivalent responses. Despite recent advancements showcasing the impre…
COBIAS: Assessing the Contextual Reliability of Bias Benchmarks for Language Models
Priyanshul Govil, Hemang Jain, Vamshi Krishna Bonagiri +4
Large Language Models (LLMs) often inherit biases from the web data they are trained on, which contains stereotypes and prejudices. Current methods for evaluating and mitigating th…