Search Results for author:
Found 8 papers, 2 papers with code
AttnIO: Knowledge Graph Exploration with In-and-Out Attention Flow for Knowledge-Grounded Dialogue
Retrieving the proper knowledge relevant to conversational context is an important challenge in dialogue systems, to engage users with more informative response.
Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement
We present a principled approach to provide LLM-based evaluation with a rigorous guarantee of human agreement.
Information-Theoretic Distillation for Reference-less Summarization
The current winning recipe for automatic summarization is using proprietary large-scale language models (LLMs) such as ChatGPT as is, or imitation learning from them as teacher models.
JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models
The permanence of online content combined with the enhanced authorship identification techniques calls for stronger computational methods to protect the identity and privacy of online authorship when needed, e. g., blind reviews for scientific papers, anonymous online reviews, or anonymous interactions in the mental health forums.
STEER: Unified Style Transfer with Expert Reinforcement
We propose STEER: Unified Style Transfer with Expert Reinforcement, a unified frame-work developed to overcome the challenge of limited parallel data for style transfer.
Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing
We present Impossible Distillation, a novel framework for paraphrasing and sentence summarization, that distills a high-quality dataset and model from a low-quality teacher that itself cannot perform these tasks.
Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations
Despite their impressive capabilities, large pre-trained language models (LMs) struggle with consistent reasoning; recently, prompting LMs to generate explanations that self-guide the inference has emerged as a promising direction to amend this.
T-GAP: Learning to Walk across Time for Temporal Knowledge Graph Completion
However, most of the existing mod-els for TKG completion extend static KG embeddings that donot fully exploit TKG structure, thus lacking in 1) account-ing for temporally relevant events already residing in the lo-cal neighborhood of a query, and 2) path-based inference that facilitates multi-hop reasoning and better interpretability.
