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Vaishnavi Shrivastava

5 accepted papers

2026

Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning

Vaishnavi Shrivastava, Ahmed Hassan Awadallah, Vidhisha Balachandran, Shivam Garg, Harkirat Behl, Dimitris Papailiopoulos
ICLR 2026poster

Large language models trained with reinforcement learning on verifiable rewards often inflate response length—trading brevity for accuracy. While longer reasoning can help on hard problems, many extra tokens are filler: verbose text making little progress. We introduce GFPO (Group Filtered Policy Op…

Cited by 0Source ↗code ↗Scholar ↗
2026

Wait, Wait, Wait... Why Do Reasoning Models Loop?

Charilaos Pipis, Shivam Garg, Vasilis Kontonis, Vaishnavi Shrivastava, Akshay Krishnamurthy, Dimitris Papailiopoulos
ICML 2026spotlight

Reasoning models (e.g., DeepSeek-R1) generate long chains of thought to solve harder problems, but they often loop, repeating the same text at low temperatures or with greedy decoding. We study why this happens and what role temperature plays. With open reasoning models, we find that looping is comm…

Cited by 0Source ↗Scholar ↗
2024

Benchmarking and Improving Generator-Validator Consistency of Language Models

Xiang Lisa Li, Vaishnavi Shrivastava, Siyan Li, Tatsunori Hashimoto, Percy Liang
ICLR 2024poster

As of September 2023, ChatGPT correctly answers “what is 7+8” with 15, but when asked “7+8=15, True or False” it responds with “False”. This inconsistency between generating and validating an answer is prevalent in language models (LMs) and erodes trust. In this paper, we propose a framework for mea…

Cited by 29Source ↗PDF ↗Scholar ↗
2024

Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs

Shashank Gupta, Vaishnavi Shrivastava, Ameet Deshpande, Ashwin Kalyan, Peter Clark, Ashish Sabharwal +1
ICLR 2024poster

Recent works have showcased the ability of large-scale language models (LLMs) to embody diverse personas in their responses, exemplified by prompts like ‘_You are Yoda. Explain the Theory of Relativity._’ While this ability allows personalization of LLMs and enables human behavior simulation, its ef…

Cited by 101Source ↗PDF ↗code ↗Scholar ↗
2022

UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis

Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis
NAACL 2022long

Global models are typically trained to be as generalizable as possible. Invariance to the specific user is considered desirable since models are shared across multitudes of users. However, these models are often unable to produce personalized responses for individual users, based on their data. Cont…

Cited by 39Source ↗PDF ↗Scholar ↗