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Found 14 papers, 2 papers with code
Granite-speech: open-source speech-aware LLMs with strong English ASR capabilities
The speech-specific components are: a conformer acoustic encoder using block attention and self-conditioning trained with connectionist temporal classification, a windowed query-transformer speech modality adapter used to do temporal downsampling of the acoustic embeddings and map them to the LLM text embedding space, and LoRA adapters to further fine-tune the text LLM.
Putting It All into Context: Simplifying Agents with LCLMs
Recent advances in language model (LM) agents have demonstrated significant potential for automating complex real-world tasks.
Activated LoRA: Fine-tuned LLMs for Intrinsics
This change crucially allows aLoRA to accept the base model's KV cache of the input string, meaning that aLoRA can be instantly activated whenever needed in a chain without recomputing the cache.
Granite Embedding Models
We introduce the Granite Embedding models, a family of encoder-based embedding models designed for retrieval tasks, spanning dense-retrieval and sparse retrieval architectures, with both English and Multilingual capabilities.
A Non-autoregressive Model for Joint STT and TTS
In this paper, we take a step towards jointly modeling automatic speech recognition (STT) and speech synthesis (TTS) in a fully non-autoregressive way.
Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks
We present a comprehensive evaluation on multiple out-of-domain datasets comparing GRANITE-20B-FUNCTIONCALLING to more than 15 other best proprietary and open models.
Formally Specifying the High-Level Behavior of LLM-Based Agents
We demonstrate how the proposed framework can be used to implement recent LLM-based agents (e. g., ReACT), and show how the flexibility of our approach can be leveraged to define a new agent with more complex behavior, the Plan-Act-Summarize-Solve (PASS) agent.
Does Structure Matter? Encoding Documents for Machine Reading Comprehension
Machine reading comprehension is a challenging task especially for querying documents with deep and interconnected contexts.
Conversational Document Prediction to Assist Customer Care Agents
A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users' needs.
Agent Assist through Conversation Analysis
Customer support agents play a crucial role as an interface between an organization and its end-users.
End-to-End Spoken Language Understanding Without Full Transcripts
For our speech-to-entities experiments on the ATIS corpus, both the CTC and attention models showed impressive ability to skip non-entity words: there was little degradation when trained on just entities versus full transcripts.
Implicit Discourse Relation Classification: We Need to Talk about Evaluation
Implicit relation classification on Penn Discourse TreeBank (PDTB) 2. 0 is a common benchmark task for evaluating the understanding of discourse relations.
The Eighth Dialog System Technology Challenge
This paper introduces the Eighth Dialog System Technology Challenge.
Generating Dialogue Agents via Automated Planning
Dialogue systems have many applications such as customer support or question answering.
