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Found 49 papers, 29 papers with code
S^2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers
The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing.
START: Self-taught Reasoner with Tools
In this paper, we introduce START (Self-Taught Reasoner with Tools), a novel tool-integrated long CoT reasoning LLM that significantly enhances reasoning capabilities by leveraging external tools.
Multi-Agent Collaboration for Multilingual Code Instruction Tuning
Recent advancement in code understanding and generation demonstrates that code LLMs fine-tuned on a high-quality instruction dataset can gain powerful capabilities to address wide-ranging code-related tasks.
Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation
First-order logic (FOL) reasoning, which involves sequential deduction, is pivotal for intelligent systems and serves as a valuable task for evaluating reasoning capabilities, particularly in chain-of-thought (CoT) contexts.
CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings
CodeElo benchmark is mainly based on the official CodeForces platform and tries to align with the platform as much as possible.
Qwen2.5 Technical Report
In addition, for hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2. 5-Turbo and Qwen2. 5-Plus, both available from Alibaba Cloud Model Studio.
Ranked #6 on
on GPQA
ExecRepoBench: Multi-level Executable Code Completion Evaluation
Code completion has become an essential tool for daily software development.
Evaluating and Aligning CodeLLMs on Human Preference
Code large language models (codeLLMs) have made significant strides in code generation.
Qwen2.5-Coder Technical Report
In this report, we introduce the Qwen2. 5-Coder series, a significant upgrade from its predecessor, CodeQwen1. 5.
Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
This RM is then applied to the iterative evolution of data in supervised fine-tuning (SFT).
Ranked #2 on
Math Word Problem Solving
on MATH
(using extra training data)
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web
We have conducted extensive experiments on existing large multimodal models, offering insights into their performance and areas for improvement in image-to-web domain.
OLMoE: Open Mixture-of-Experts Language Models
We introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE).
Synthesizing Text-to-SQL Data from Weak and Strong LLMs
The capability gap between open-source and closed-source large language models (LLMs) remains a challenge in text-to-SQL tasks.
OpenHands: An Open Platform for AI Software Developers as Generalist Agents
OpenDevin), a platform for the development of powerful and flexible AI agents that interact with the world in similar ways to those of a human developer: by writing code, interacting with a command line, and browsing the web.
Qwen2 Technical Report
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Ranked #2 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
Fulfilling both of these characteristics can pose a great challenge for LLMs. To assess how well LLMs can solve challenging and practical tasks via programs, we introduce BigCodeBench, a benchmark that challenges LLMs to invoke multiple function calls as tools from 139 libraries and 7 domains for 1, 140 fine-grained tasks.
Ranked #1 on
Code Generation
on BigCodeBench-Instruct
DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception
The development of large language models (LLMs) has significantly advanced the emergence of large multimodal models (LMMs).
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study
Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities.
StarCoder 2 and The Stack v2: The Next Generation
Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size.
Ranked #35 on
Code Generation
on MBPP
DialCLIP: Empowering CLIP as Multi-Modal Dialog Retriever
Recently, substantial advancements in pre-trained vision-language models have greatly enhanced the capabilities of multi-modal dialog systems.
One-Shot Learning as Instruction Data Prospector for Large Language Models
Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may compromise model performance.
Lemur: Harmonizing Natural Language and Code for Language Agents
We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents.
An Investigation of LLMs' Inefficacy in Understanding Converse Relations
Our ConvRE features two tasks, Re2Text and Text2Re, which are formulated as multi-choice question answering to evaluate LLMs' ability to determine the matching between relations and associated text.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
VDialogUE: A Unified Evaluation Benchmark for Visually-grounded Dialogue
Visually-grounded dialog systems, which integrate multiple modes of communication such as text and visual inputs, have become an increasingly popular area of investigation.
Model Inversion Attack via Dynamic Memory Learning
Model Inversion (MI) attacks aim to recover the private training data from the target model, which has raised security concerns about the deployment of DNNs in practice.
OctoPack: Instruction Tuning Code Large Language Models
We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46. 2% pass@1).
Ranked #19 on
Code Generation
on HumanEval
A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment
Training large language models (LLMs) with open-domain instruction data has yielded remarkable success in aligning to end tasks and human preferences.
PolyLM: An Open Source Polyglot Large Language Model
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions.
History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling
Context information modeling is an important task in conversational KBQA.
Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark
Existing multimodal task-oriented dialog data fails to demonstrate the diverse expressions of user subjective preferences and recommendation acts in the real-life shopping scenario.
PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts
It utilizes a combination of several fundamental experts to accommodate multiple dialogue-related tasks and can be pre-trained using limited dialogue and extensive non-dialogue multi-modal data.
Ranked #1 on
Response Generation
on SIMMC2.0
Iterative Forward Tuning Boosts In-Context Learning in Language Models
Despite the advancements in in-context learning (ICL) for large language models (LLMs), current research centers on specific prompt engineering, such as demonstration selection, with the expectation that a single iteration of demonstrations processing can generalize effectively to a given test sample.
Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts
Out-of-Domain (OOD) intent detection is vital for practical dialogue systems, and it usually requires considering multi-turn dialogue contexts.
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs
Our emphasis on database values highlights the new challenges of dirty database contents, external knowledge between NL questions and database contents, and SQL efficiency, particularly in the context of massive databases.
Ranked #1 on
Text-To-SQL
on BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation)
(Execution Accurarcy (Human) metric)
Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
To alleviate the above challenges, we exploit large language models (LLMs) as decomposers for effective table-based reasoning, which (i) decompose huge evidence (a huge table) into sub-evidence (a small table) to mitigate the interference of useless information for table reasoning; and (ii) decompose complex questions into simpler sub-questions for text reasoning.
Ranked #2 on
Table-based Fact Verification
on TabFact
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing
Recently, the pre-trained text-to-text transformer model, namely T5, though not specialized for text-to-SQL parsing, has achieved state-of-the-art performance on standard benchmarks targeting domain generalization.
Ranked #4 on
Semantic Parsing
on spider
SPRING: Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph
Existing multimodal conversation agents have shown impressive abilities to locate absolute positions or retrieve attributes in simple scenarios, but they fail to perform well when complex relative positions and information alignments are involved, which poses a bottleneck in response quality.
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing
Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation.
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers
To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other.
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding
Pre-training methods with contrastive learning objectives have shown remarkable success in dialog understanding tasks.
Latent Heterogeneous Graph Network for Incomplete Multi-View Learning
Multi-view learning has progressed rapidly in recent years.
A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions
In recent years, deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output SQL query.
Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing
The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i. e., properly recognizing mentions of unseen columns or tables when generating SQLs.
S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers
The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing.
Linking-Enhanced Pre-Training for Table Semantic Parsing
Recently pre-training models have significantly improved the performance of various NLP tasks by leveraging large-scale text corpora to improve the contextual representation ability of the neural network.
Improving Text-to-SQL with Schema Dependency Learning
In this paper, we present the Schema Dependency guided multi-task Text-to-SQL model (SDSQL) to guide the network to effectively capture the interactions between questions and schemas.
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
Semantic parsing has long been a fundamental problem in natural language processing.
Ranked #5 on
Dialogue State Tracking
on CoSQL
Multi-view Deep Subspace Clustering Networks
Dnet learns view-specific self-representation matrices, whereas Unet learns a common self-representation matrix for all views.
Ranked #1 on
Multi-view Subspace Clustering
on ORL
