activity
20162026
most citedLaMDA: Language Models for Dialog Applications

708 citations · 1.7k across the 20 of their papers we have counts for

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17 papers · 1 filter

cs.CL2026

Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size

Dikshant Kukreja, Kshitij Sah, Gautam Gupta +5

Larger language models become simultaneously better and worse at handling contextual information -- better at ignoring false claims, worse at ignoring irrelevant tokens. We formali…

cs.CL2025★ 49 cited

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…

cs.CL2025★ 65 cited

Gemma 3 Technical Report

Gemma Team, Aishwarya Kamath, Johan Ferret +209

We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision underst…

cs.CL2024★ 1 cited

Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation

Kartik Kartik, Sanjana Soni, Anoop Kunchukuttan +2

The widespread online communication in a modern multilingual world has provided opportunities to blend more than one language (aka code-mixed language) in a single utterance. This…

cs.CL2024★ 1 cited

"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing

Vipula Rawte, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman +4

Hallucination has emerged as the most vulnerable aspect of contemporary Large Language Models (LLMs). In this paper, we introduce the Sorry, Come Again (SCA) prompting, aimed to av…

cs.CL2024

Naive Bayes-based Context Extension for Large Language Models

Jianlin Su, Murtadha Ahmed, Wenbo +3

Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations…