695 citations · 2.7k across the 76 of their papers we have counts for
86 papers
Lost in Simulation: LLM-Simulated Users are Unreliable Proxies for Human Users in Agentic Evaluations
Preethi Seshadri, Samuel Cahyawijaya, Ayomide Odumakinde +2
Agentic benchmarks increasingly rely on LLM-simulated users to scalably evaluate agent performance, yet the robustness, validity, and fairness of this approach remain unexamined. T…
Entropy-Aligned Decoding of LMs for Better Writing and Reasoning
Kareem Ahmed, Sameer Singh
Language models (LMs) are trained on billions of tokens in an attempt to recover the true language distribution. Still, vanilla random sampling from LMs yields low quality generati…
Characterizing Mamba's Selective Memory using Auto-Encoders
Tamanna Hossain, Robert L. Logan, Ganesh Jagadeesan +3
State space models (SSMs) are a promising alternative to transformers for language modeling because they use fixed memory during inference. However, this fixed memory usage require…
Parallel Token Prediction for Language Models
Felix Draxler, Justus Will, Farrin Marouf Sofian +3
Autoregressive decoding in language models is inherently slow, generating only one token per forward pass. We propose Parallel Token Prediction (PTP), a general-purpose framework f…
Leveraging In-Context Learning for Language Model Agents
Shivanshu Gupta, Sameer Singh, Ashish Sabharwal +2
In-context learning (ICL) with dynamically selected demonstrations combines the flexibility of prompting large language models (LLMs) with the ability to leverage training data to…
Semantic Probabilistic Control of Language Models
Kareem Ahmed, Catarina G Belem, Padhraic Smyth +1
Semantic control entails steering LM generations towards satisfying subtle non-lexical constraints, e.g., toxicity, sentiment, or politeness, attributes that can be captured by a s…