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Humans and LLMs Diverge on Probabilistic Inferences
Gaurav Kamath, Sreenath Madathil, Sebastian Schuster +2
Human reasoning often involves working over limited information to arrive at probabilistic conclusions. In its simplest form, this involves making an inference that is not strictly…
Value Drifts: Tracing Value Alignment During LLM Post-Training
Mehar Bhatia, Shravan Nayak, Gaurav Kamath +4
As LLMs occupy an increasingly important role in society, they are more and more confronted with questions that require them not only to draw on their general knowledge but also to…
Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?
Gaurav Kamath, Sowmya Vajjala
Named Entity Recognition(NER) for low-resource languages aims to produce robust systems for languages where there is limited labeled training data available, and has been an area o…
DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
Sara Vera Marjanović, Arkil Patel, Vaibhav Adlakha +14
Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an answer for a given input, DeepSeek-R1 creat…
Language Models Largely Exhibit Human-like Constituent Ordering Preferences
Ada Defne Tur, Gaurav Kamath, Siva Reddy
Though English sentences are typically inflexible vis-à-vis word order, constituents often show far more variability in ordering. One prominent theory presents the notion that cons…
Scope Ambiguities in Large Language Models
Gaurav Kamath, Sebastian Schuster, Sowmya Vajjala +1
Sentences containing multiple semantic operators with overlapping scope often create ambiguities in interpretation, known as scope ambiguities. These ambiguities offer rich insight…