2 citations · 2 across the 5 of their papers we have counts for
5 papers
GAR: Carbon-Aware Routing for LLM Inference via Constrained Optimization
Disha Sheshanarayana, Rajat Subhra Pal, Manjira Sinha +1
The growing deployment of large language models (LLMs) makes per-request routing essential for balancing response quality and computational cost across heterogeneous model pools. C…
Thinking in Latents: Adaptive Anchor Refinement for Implicit Reasoning in LLMs
Disha Sheshanarayana, Rajat Subhra Pal, Manjira Sinha +1
Token-level Chain-of-Thought (CoT) prompting has become a standard way to elicit multi-step reasoning in large language models (LLMs), especially for mathematical word problems. Ho…
ProofSketch: Efficient Verified Reasoning for Large Language Models
Disha Sheshanarayana, Tanishka Magar
Reasoning methods such as chain-of-thought prompting and self-consistency have shown immense potential to improve the accuracy of large language models across various reasoning tas…
CLAIM: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues
Disha Sheshanarayana, Tanishka Magar, Ayushi Mittal +1
Courtrooms are places where lives are determined and fates are sealed, yet they are not impervious to manipulation. Strategic use of manipulation in legal jargon can sway the opini…
HeCiX: Integrating Knowledge Graphs and Large Language Models for Biomedical Research
Prerana Sanjay Kulkarni, Muskaan Jain, Disha Sheshanarayana +1
Despite advancements in drug development strategies, 90% of clinical trials fail. This suggests overlooked aspects in target validation and drug optimization. In order to address t…