REVIEW 10 cited by
LLaSM: Large Language and Speech Model
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
LLaSM: Large Language and Speech Model
read the original abstract
Multi-modal large language models have garnered significant interest recently. Though, most of the works focus on vision-language multi-modal models providing strong capabilities in following vision-and-language instructions. However, we claim that speech is also an important modality through which humans interact with the world. Hence, it is crucial for a general-purpose assistant to be able to follow multi-modal speech-and-language instructions. In this work, we propose Large Language and Speech Model (LLaSM). LLaSM is an end-to-end trained large multi-modal speech-language model with cross-modal conversational abilities, capable of following speech-and-language instructions. Our early experiments show that LLaSM demonstrates a more convenient and natural way for humans to interact with artificial intelligence. Specifically, we also release a large Speech Instruction Following dataset LLaSM-Audio-Instructions. Code and demo are available at https://github.com/LinkSoul-AI/LLaSM and https://huggingface.co/spaces/LinkSoul/LLaSM. The LLaSM-Audio-Instructions dataset is available at https://huggingface.co/datasets/LinkSoul/LLaSM-Audio-Instructions.
Forward citations
Cited by 10 Pith papers
-
Hijacking Large Audio-Language Models via Context-Agnostic and Imperceptible Auditory Prompt Injection
AudioHijack generates imperceptible adversarial audio via gradient estimation, attention supervision, and reverberation blending to hijack 13 LALMs with 79-96% success on unseen contexts and real commercial agents.
-
MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks
MECAT is a multi-expert benchmark for audio AI offering fine-grained captions and QA pairs generated via expert models and LLM reasoning, paired with the DATE metric that combines semantic similarity and cross-sample ...
-
Is Text All You Need? Text as a Universal Information Bottleneck for Speech LLMs
C-Gate represents speech frames as convex combinations of LLM token embeddings to enforce manifold compatibility, delivering up to 48.7% relative WER reduction on LibriSpeech while preserving emotion recognition accuracy.
-
Continuous Audio Thinking for Large Audio Language Models
CoAT adds a continuous latent thinking space to LALMs via expert distillation to retain acoustic information, yielding gains on audio reasoning, understanding, music, emotion, and transcription benchmarks across three models.
-
A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff
A synonymous source coding architecture and variational inference framework derive the rate-distortion-perception tradeoff by treating perception as recovery of any admissible synonymous sample.
-
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
Qwen-Audio trains a unified model on diverse audio and tasks with hierarchical tags to enable strong zero-shot performance on audio understanding benchmarks and multi-turn audio chat.
-
A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff
Synset-based reconstruction and synonymous variational inference are claimed to derive the distributional divergence in RDP and unify it with classical rate-distortion theory.
-
Enhancing Speech Large Language Models through Reinforced Behavior Alignment
Reinforced Behavior Alignment (RBA) uses self-synthesized data from a teacher LLM and reinforcement learning to close the instruction-following gap in SpeechLMs, outperforming distillation and reaching SOTA on spoken ...
-
On The Landscape of Spoken Language Models: A Comprehensive Survey
A literature survey that organizes spoken language models by architecture, training, and evaluation choices and identifies key challenges and future directions.
-
Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
The paper provides the first comprehensive survey of multimodal chain-of-thought reasoning, including foundational concepts, a taxonomy of methodologies, application analyses, challenges, and future directions.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.