Papers by Sricharan Kumar
SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for hallucination detection rely on self-consistency check alone . prominent LMs exhibit a tendency to produce exceedingly confident, but erroneous, assertions . |
| Approach: | They propose a sampling-based method that expands on the principle of self-consistency checking to detect hallucinations at question-level and model-level. |
| Outcome: | The proposed method outperforms the state of the art in detecting non-factual and factual statements across multiple question-answering and open-domain generation benchmarks. |
Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models capture factual knowledge across a wide range of domains, but refining their capabilities on previously seen knowledge remains a challenge. |
| Approach: | They propose a synthetic knowledge ingestion method that leverages fine-grained synthesis and interleaved generation to construct high-quality data representations from raw knowledge sources. |
| Outcome: | The proposed method outperforms baseline methods on question-answering tasks spanning finance, biomedicine, and open-generation domains. |
Heuristic-based Search Algorithm in Automatic Instruction-focused Prompt Optimization: A Survey (2025.findings-acl)
Copied to clipboard
Wendi Cui, Jiaxin Zhang, Zhuohang Li, Hao Sun, Damien Lopez, Kamalika Das, Bradley A. Malin, Sricharan Kumar
| Challenge: | Recent advances in Large Language Models (LLMs) have led to remarkable achievements across a variety of NLP tasks. |
| Approach: | They propose a taxonomy of automatic prompt optimization methods that explore and improve prompts with minimal human oversight. |
| Outcome: | The proposed methods can explore and improve prompts with minimal human oversight. |
SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization (2025.acl-long)
Copied to clipboard
Wendi Cui, Jiaxin Zhang, Zhuohang Li, Hao Sun, Damien Lopez, Kamalika Das, Bradley A. Malin, Sricharan Kumar
| Challenge: | Existing approaches separate the optimization of prompt instructions and in-context learning examples, leading to incohesive, suboptimal results. |
| Approach: | They propose a framework that refines both prompt instructions and in-context learning examples. |
| Outcome: | The proposed framework outperforms state-of-the-art prompt optimization methods on 35 benchmark tasks. |
Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation (2024.emnlp-main)
Copied to clipboard
Zhuohang Li, Jiaxin Zhang, Chao Yan, Kamalika Das, Sricharan Kumar, Murat Kantarcioglu, Bradley Malin
| Challenge: | Language models suffer from poor interpretability and transparency, as well as the intrinsic risk of hallucination and misinformation. |
| Approach: | They propose a statistical framework that assesses how well a query can be answered by an RAG system by capturing the relevance of knowledge. |
| Outcome: | The proposed framework assesses how well a query can be answered by an RAG system by capturing the relevance of knowledge. |
Divide-Conquer-Reasoning for Consistency Evaluation and Automatic Improvement of Large Language Models (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Existing methods for evaluating the quality and consistency of text generated by Large Language Models are not effective. |
| Approach: | They propose a divide-conquer-reasoning approach to evaluate LLM-generated texts using a split-and-conquers evaluator and an automatic metric converter to facilitate this approach. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by a large margin on multiple benchmarks and reduces 90% of output inconsistencies in one iteration. |