Search Results for author:
Found 15 papers, 8 papers with code
Training Language Models to Generate Text with Citations via Fine-grained Rewards
While recent Large Language Models (LLMs) have proven useful in answering user queries, they are prone to hallucination, and their responses often lack credibility due to missing references to reliable sources.
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Our framework trains a single arbitrary LM that adaptively retrieves passages on-demand, and generates and reflects on retrieved passages and its own generations using special tokens, called reflection tokens.
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?
The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fact that most data is protected from public release for privacy reasons.
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
We introduce Fine-Grained RLHF, a framework that enables training and learning from reward functions that are fine-grained in two respects: (1) density, providing a reward after every segment (e. g., a sentence) is generated; and (2) incorporating multiple reward models associated with different feedback types (e. g., factual incorrectness, irrelevance, and information incompleteness).
INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions
In an information-seeking conversation, a user may ask questions that are under-specified or unanswerable.
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each question needs to be interpreted within the dialogue context.
DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization
Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation.
Automatic Document Sketching: Generating Drafts from Analogous Texts
The advent of large pre-trained language models has made it possible to make high-quality predictions on how to add or change a sentence in a document.
Extracting Summary Knowledge Graphs from Long Documents
Knowledge graphs capture entities and relations from long documents and can facilitate reasoning in many downstream applications.
A Controllable Model of Grounded Response Generation
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses.
HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion
Taxonomies are of great value to many knowledge-rich applications.
SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble
In this study, we propose a novel framework, SetExpan, which tackles this problem, with two techniques: (1) a context feature selection method that selects clean context features for calculating entity-entity distributional similarity, and (2) a ranking-based unsupervised ensemble method for expanding entity set based on denoised context features.
Indirect Supervision for Relation Extraction using Question-Answer Pairs
However, due to the incompleteness of knowledge bases and the context-agnostic labeling, the training data collected via distant supervision (DS) can be very noisy.
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations.
Ranked #11 on
Relation Extraction
on NYT11-HRL
