Papers by Preetam Prabhu Srikar Dammu

3 papers
ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs (2024.findings-emnlp)

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Challenge: Despite the fact that many fact-checking tools lack granularity and explainability, they lack the ability to be useful in various contexts.
Approach: They propose a text validation framework that provides granular explanations for each claim and localizes the specific problematic content to reduce cognitive load.
Outcome: The proposed framework provides granular explanations for each claim prediction and localizes and educates users on the specific content.
“They are uncultured”: Unveiling Covert Harms and Social Threats in LLM Generated Conversations (2024.emnlp-main)

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Challenge: Prior studies on LLM harms focus on Western concepts like race and gender, overlooking cultural concepts from other parts of the world.
Approach: They propose a set of seven metrics to examine the presence of covert harms in LLM-generated conversations.
Outcome: The proposed model detects that seven out of eight LLMs generated conversations riddled with CHAST, characterized by malign views expressed in seemingly neutral language, compared to Western ones such as race.
ClaimDB: A Fact Verification Benchmark over Large Structured Data (2026.acl-long)

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Challenge: despite substantial progress in fact-verification benchmarks, this setting remains largely underexplored.
Approach: They propose a fact-verification benchmark where evidence for claims is derived from compositions of millions of records and multiple tables.
Outcome: The proposed benchmarks score below 55% accuracy with 30 state-of-the-art LLMs and are released on github.

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