Search Results for author:
Found 14 papers, 10 papers with code
Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks
We validate CR-Planner on challenging domain-knowledge-intensive and reasoning-heavy tasks, including competitive programming, theorem-driven math reasoning, and complex domain retrieval problems.
SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages
Large Language Models (LLMs) have shown remarkable abilities across various tasks, yet their development has predominantly centered on high-resource languages like English and Chinese, leaving low-resource languages underserved.
On the Transformations across Reward Model, Parameter Update, and In-Context Prompt
Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications.
Reasons to Reject? Aligning Language Models with Judgments
CUT (LLaMA2-chat-13b) can also align LLMs in an iterative fashion using up-to-date model-specific judgments, improving performance from 81. 09 to 91. 68 points on AlpacaEval.
mPMR: A Multilingual Pre-trained Machine Reader at Scale
mPMR aims to guide multilingual pre-trained language models (mPLMs) to perform natural language understanding (NLU) including both sequence classification and span extraction in multiple languages.
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine Reader
We present Pre-trained Machine Reader (PMR), a novel method for retrofitting pre-trained masked language models (MLMs) to pre-trained machine reading comprehension (MRC) models without acquiring labeled data.
PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks
Specifically, a novel Peer Data Augmentation (PeerDA) approach is proposed which employs span pairs with the PR relation as the augmentation data for training.
ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction
We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts.
A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems
In this work, we propose a novel Unified MultI-goal conversational recommeNDer system, namely UniMIND.
Improving Lexical Embeddings for Robust Question Answering
Recent techniques in Question Answering (QA) have gained remarkable performance improvement with some QA models even surpassed human performance.
Exploiting Reasoning Chains for Multi-hop Science Question Answering
We propose a novel Chain Guided Retriever-reader ({\tt CGR}) framework to model the reasoning chain for multi-hop Science Question Answering.
Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering
Our framework contains three new ideas: (a) {\tt AMR-SG}, an AMR-based Semantic Graph, constructed by candidate fact AMRs to uncover any hop relations among question, answer and multiple facts.
Addressing the Vulnerability of NMT in Input Perturbations
Neural Machine Translation (NMT) has achieved significant breakthrough in performance but is known to suffer vulnerability to input perturbations.
Revisit Automatic Error Detection for Wrong and Missing Translation -- A Supervised Approach
While achieving great fluency, current machine translation (MT) techniques are bottle-necked by adequacy issues.
