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Found 60 papers, 38 papers with code
Integrating Transformers and Knowledge Graphs for Twitter Stance Detection
Stance detection (SD) entails classifying the sentiment of a text towards a given target, and is a relevant sub-task for opinion mining and social media analysis.
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games
Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed environments and large action spaces.
On the Effect of Isotropy on VAE Representations of Text
Injecting desired geometric properties into text representations has attracted a lot of attention.
VerifiAgent: a Unified Verification Agent in Language Model Reasoning
Large language models demonstrate remarkable reasoning capabilities but often produce unreliable or incorrect responses.
SpeechDialogueFactory: Generating High-Quality Speech Dialogue Data to Accelerate Your Speech-LLM Development
High-quality speech dialogue datasets are crucial for Speech-LLM development, yet existing acquisition methods face significant limitations.
ReasonGraph: Visualisation of Reasoning Paths
Large Language Models (LLMs) reasoning processes are challenging to analyze due to their complexity and the lack of organized visualization tools.
Assessing the Alignment of FOL Closeness Metrics with Human Judgement
Using ground-truth FOLs, we carefully designed various perturbations on the ground-truth to assess metric sensitivity.
Methods for Legal Citation Prediction in the Age of LLMs: An Australian Law Case Study
In recent years, Large Language Models (LLMs) have shown great potential across a wide range of legal tasks.
One STEP at a time: Language Agents are Stepwise Planners
We introduce STEP, a novel framework designed to efficiently learn from previous experiences to enhance the planning capabilities of language agents in future steps.
Audio Is the Achilles' Heel: Red Teaming Audio Large Multimodal Models
Our results under these settings demonstrate that open-source audio LMMs suffer an average attack success rate of 69. 14% on harmful audio questions, and exhibit safety vulnerabilities when distracted with non-speech audio noise.
Jigsaw Puzzles: Splitting Harmful Questions to Jailbreak Large Language Models
Moreover, JSP achieves a state-of-the-art attack success rate of 92% on GPT-4 on the harmful query benchmark, and exhibits strong resistant to defence strategies.
Aligning with Logic: Measuring, Evaluating and Improving Logical Preference Consistency in Large Language Models
Large Language Models (LLMs) are expected to be predictable and trustworthy to support reliable decision-making systems.
Strategies for Improving NL-to-FOL Translation with LLMs: Data Generation, Incremental Fine-Tuning, and Verification
Research in symbolic logical reasoning explored FOL generation using state-of-the-art LLMs (i. e., GPT-4) to produce FOL translations of natural language (NL) statements, but errors in translation are usually not the focus.
The Compressor-Retriever Architecture for Language Model OS
Recent advancements in large language models (LLMs) have significantly enhanced their capacity to aggregate and process information across multiple modalities, enabling them to perform a wide range of tasks such as multimodal data querying, tool usage, web interactions, and handling long documents.
Towards Probing Speech-Specific Risks in Large Multimodal Models: A Taxonomy, Benchmark, and Insights
Based on the taxonomy, we create a small-scale dataset for evaluating current LMMs capability in detecting these categories of risk.
Can LLMs Reason in the Wild with Programs?
To investigate this, we introduce the task of reasoning in the wild, where an LLM is tasked to solve a reasoning problem of unknown type by identifying the subproblems and their corresponding formalisms, and writing a program to solve each subproblem, guided by a tactic.
Exploring the Potential of Multimodal LLM with Knowledge-Intensive Multimodal ASR
Recent advancements in multimodal large language models (MLLMs) have made significant progress in integrating information across various modalities, yet real-world applications in educational and scientific domains remain challenging.
A Closer Look at Logical Reasoning with LLMs: The Choice of Tool Matters
This paradigm has established the current state-of-the-art result in logical reasoning (i. e., deductive reasoning).
Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators
Large Language Models (LLMs) have demonstrated promising capabilities as automatic evaluators in assessing the quality of generated natural language.
Human Brain Exhibits Distinct Patterns When Listening to Fake Versus Real Audio: Preliminary Evidence
In this paper we study the variations in human brain activity when listening to real and fake audio.
Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models
Simultaneous machine translation (SimulMT) presents a challenging trade-off between translation quality and latency.
Unlocking Structure Measuring: Introducing PDD, an Automatic Metric for Positional Discourse Coherence
Recent large language models (LLMs) have shown remarkable performance in aligning generated text with user intentions across various tasks.
Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance
Large language models (LLMs) have exhibited an array of reasoning capabilities but face challenges like error propagation and hallucination, particularly in specialised areas like finance, where data is heterogeneous, and precision is paramount.
Towards Uncertainty-Aware Language Agent
We present the Uncertainty-Aware Language Agent (UALA), a framework that orchestrates the interaction between the agent and the external world using uncertainty quantification.
Instruct-SCTG: Guiding Sequential Controlled Text Generation through Instructions
Instruction-tuned large language models have shown remarkable performance in aligning generated text with user intentions across various tasks.
POSQA: Probe the World Models of LLMs with Size Comparisons
Embodied language comprehension emphasizes that language understanding is not solely a matter of mental processing in the brain but also involves interactions with the physical and social environment.
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.
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Reward Engineering for Generating Semi-structured Explanation
Semi-structured explanation depicts the implicit process of a reasoner with an explicit representation.
Simultaneous Machine Translation with Large Language Models
The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
Investigating Pre-trained Audio Encoders in the Low-Resource Condition
Pre-trained speech encoders have been central to pushing state-of-the-art results across various speech understanding and generation tasks.
Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation
Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature.
PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs
We show how a small language model could be trained to act as a verifier module for the output of an LLM~(i. e., ChatGPT, GPT-4), and to iteratively improve its performance via fine-grained corrective instructions.
A Minimal Approach for Natural Language Action Space in Text-based Games
Text-based games (TGs) are language-based interactive environments for reinforcement learning.
Koala: An Index for Quantifying Overlaps with Pre-training Corpora
In very recent years more attention has been placed on probing the role of pre-training data in Large Language Models (LLMs) downstream behaviour.
Plug-and-Play Recipe Generation with Content Planning
Specifically, it optimizes the joint distribution of the natural language sequence and the global content plan in a plug-and-play manner.
Self-supervised Rewiring of Pre-trained Speech Encoders: Towards Faster Fine-tuning with Less Labels in Speech Processing
Pre-trained speech Transformers have facilitated great success across various speech processing tasks.
Self-supervised Graph Masking Pre-training for Graph-to-Text Generation
Large-scale pre-trained language models (PLMs) have advanced Graph-to-Text (G2T) generation by processing the linearised version of a graph.
RedApt: An Adaptor for wav2vec 2 Encoding \\ Faster and Smaller Speech Translation without Quality Compromise
Pre-trained speech Transformers in speech translation (ST) have facilitated state-of-the-art (SotA) results; yet, using such encoders is computationally expensive.
Generating Synthetic Speech from SpokenVocab for Speech Translation
Training end-to-end speech translation (ST) systems requires sufficiently large-scale data, which is unavailable for most language pairs and domains.
On Reality and the Limits of Language Data: Aligning LLMs with Human Norms
Recent advancements in Large Language Models (LLMs) harness linguistic associations in vast natural language data for practical applications.
M-Adapter: Modality Adaptation for End-to-End Speech-to-Text Translation
End-to-end speech-to-text translation models are often initialized with pre-trained speech encoder and pre-trained text decoder.
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning
Masked language models (MLMs) such as BERT and RoBERTa have revolutionized the field of Natural Language Understanding in the past few years.
Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models
To catalyse the research in this direction, we release a well-curated biomedical knowledge probing benchmark, MedLAMA, which is constructed based on the Unified Medical Language System (UMLS) Metathesaurus.
The Neglected Sibling: Isotropic Gaussian Posterior for VAE
Deep generative models have been widely used in several areas of NLP, and various techniques have been proposed to augment them or address their training challenges.
It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpretation Data
Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora.
Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT
Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks.
Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets
To highlight the challenges of achieving representation disentanglement for text domain in an unsupervised setting, in this paper we select a representative set of successfully applied models from the image domain.
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification
Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP.
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters
Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT.
Self-Alignment Pretraining for Biomedical Entity Representations
Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge.
COMETA: A Corpus for Medical Entity Linking in the Social Media
Whilst there has been growing progress in Entity Linking (EL) for general language, existing datasets fail to address the complex nature of health terminology in layman's language.
Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders
It has been long known that sparsity is an effective inductive bias for learning efficient representation of data in vectors with fixed dimensionality, and it has been explored in many areas of representation learning.
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation
While the explicit constraint naturally avoids posterior collapse, we use it to further understand the significance of the KL term in controlling the information transmitted through the VAE channel.
Bayesian Learning for Neural Dependency Parsing
While neural dependency parsers provide state-of-the-art accuracy for several languages, they still rely on large amounts of costly labeled training data.
Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines
In recent years neural language models (LMs) have set the state-of-the-art performance for several benchmarking datasets.
Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees
Efficient methods for storing and querying are critical for scaling high-order n-gram language models to large corpora.
Structured Prediction of Sequences and Trees using Infinite Contexts
Linguistic structures exhibit a rich array of global phenomena, however commonly used Markov models are unable to adequately describe these phenomena due to their strong locality assumptions.
