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Found 31 papers, 23 papers with code
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
Can Language Models Solve Olympiad Programming?
In this paper, we introduce the USACO benchmark with 307 problems from the USA Computing Olympiad, along with high-quality unit tests, reference code, and official analyses for each problem.
DevBench: A Comprehensive Benchmark for Software Development
Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities.
OS-Copilot: Towards Generalist Computer Agents with Self-Improvement
Autonomous interaction with the computer has been a longstanding challenge with great potential, and the recent proliferation of large language models (LLMs) has markedly accelerated progress in building digital agents.
Large Language Model for Multi-objective Evolutionary Optimization
It is also promising to see the operator only learned from a few instances can have robust generalization performance on unseen problems with quite different patterns and settings.
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models.
Ranked #2 on
Bug fixing
on SWE-bench
FireAct: Toward Language Agent Fine-tuning
Recent efforts have augmented language models (LMs) with external tools or environments, leading to the development of language agents that can reason and act.
Ranked #7 on
Question Answering
on Bamboogle
Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve
This approach - which we call the teleological approach - leads us to identify three factors that we hypothesize will influence LLM accuracy: the probability of the task to be performed, the probability of the target output, and the probability of the provided input.
Cognitive Architectures for Language Agents
Recent efforts have augmented large language models (LLMs) with external resources (e. g., the Internet) or internal control flows (e. g., prompt chaining) for tasks requiring grounding or reasoning, leading to a new class of language agents.
COLLIE: Systematic Construction of Constrained Text Generation Tasks
Text generation under constraints have seen increasing interests in natural language processing, especially with the rapidly improving capabilities of large language models.
InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback
Our framework is language and platform agnostic, uses self-contained Docker environments to provide safe and reproducible execution, and is compatible out-of-the-box with traditional seq2seq coding methods, while enabling the development of new methods for interactive code generation.
Referral Augmentation for Zero-Shot Information Retrieval
We propose Referral-Augmented Retrieval (RAR), a simple technique that concatenates document indices with referrals, i. e. text from other documents that cite or link to the given document, to provide significant performance gains for zero-shot information retrieval.
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference.
Personality Understanding of Fictional Characters during Book Reading
As readers engage with a story, their understanding of a character evolves based on new events and information; and multiple fine-grained aspects of personalities can be perceived.
EC^2: Emergent Communication for Embodied Control
We learn embodied representations of video trajectories, emergent language, and natural language using a language model, which is then used to finetune a lightweight policy network for downstream control.
Reflexion: Language Agents with Verbal Reinforcement Learning
Large language models (LLMs) have been increasingly used to interact with external environments (e. g., games, compilers, APIs) as goal-driven agents.
EC2: Emergent Communication for Embodied Control
We learn embodied representations of video trajectories, emergent language, and natural language using a language model, which is then used to finetune a lightweight policy network for downstream control.
Revisiting the Roles of "Text" in Text Games
Text games present opportunities for natural language understanding (NLU) methods to tackle reinforcement learning (RL) challenges.
ReAct: Synergizing Reasoning and Acting in Language Models
While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e. g. chain-of-thought prompting) and acting (e. g. action plan generation) have primarily been studied as separate topics.
WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
Existing benchmarks for grounding language in interactive environments either lack real-world linguistic elements, or prove difficult to scale up due to substantial human involvement in the collection of data or feedback signals.
TVShowGuess: Character Comprehension in Stories as Speaker Guessing
We propose a new task for assessing machines' skills of understanding fictional characters in narrative stories.
Linking Emergent and Natural Languages via Corpus Transfer
In this work, we propose a novel way to establish such a link by corpus transfer, i. e. pretraining on a corpus of emergent language for downstream natural language tasks, which is in contrast to prior work that directly transfers speaker and listener parameters.
Multi-Stage Episodic Control for Strategic Exploration in Text Games
Text adventure games present unique challenges to reinforcement learning methods due to their combinatorially large action spaces and sparse rewards.
DFA-NeRF: Personalized Talking Head Generation via Disentangled Face Attributes Neural Rendering
Specifically, neural radiance field takes lip movements features and personalized attributes as two disentangled conditions, where lip movements are directly predicted from the audio inputs to achieve lip-synchronized generation.
Calibrated RGB-D Salient Object Detection
Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD).
Ranked #13 on
Thermal Image Segmentation
on RGB-T-Glass-Segmentation
Self-Attention Networks Can Process Bounded Hierarchical Languages
Despite their impressive performance in NLP, self-attention networks were recently proved to be limited for processing formal languages with hierarchical structure, such as $\mathsf{Dyck}_k$, the language consisting of well-nested parentheses of $k$ types.
Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents
Text-based games simulate worlds and interact with players using natural language.
Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection
Our bidirectional dynamic fusion strategy encourages the interaction of spatial and temporal information in a dynamic manner.
Ranked #12 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Keep CALM and Explore: Language Models for Action Generation in Text-based Games
In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state.
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations
We also present a new test set for measuring violations of physical expectations, using a range of scenarios derived from developmental psychology.
3D-Aware Scene Manipulation via Inverse Graphics
In this work, we propose 3D scene de-rendering networks (3D-SDN) to address the above issues by integrating disentangled representations for semantics, geometry, and appearance into a deep generative model.
