Applied

Anthropic economic research on productivity gains

Welcome in 2021,I know its already been some months but I hope you had a good and motivated start into the new year. What could be better than starting by reading the newest blog post about “…

Models for zero-shot text classification that are up to 50 times faster than Cross-Encoders and show the same or higher accuracy.

To fine-tune a Sentence Transformer using triplet or contrastive loss, follow these steps: prepare data in the required

The Contradiction Psb Lds model is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space, allowing for tasks like clustering or semantic search. But what does that mean for you? It means you can use this model to identify contradiction sentences in patents with ease. It's built on top of the PatentSBERTa model and can be used with either sentence-transformers or HuggingFace Transformers. The model is efficient and can be used for a variety of tasks, but have you ever wondered how it was trained? It was trained with a batch size of 16 and a learning rate of 2e-05, with a total of 1128 steps per epoch. The model's architecture is based on the MPNetModel, which is a type of transformer model. So, what makes this model unique? It's ability to identify contradiction sentences in patents, making it a valuable tool for anyone working with patent data.

State of the Art Natural Language Processing. Contribute to JohnSnowLabs/spark-nlp development by creating an account on GitHub.

Natural language inference (NLI), also known as Recognizing Textual Entailment (RTE), is an important aspect of natural language understanding. Most research now uses machine learning and deep learning to perform this task on specific datasets, meaning their solution is not explainable nor explicit. To address the need for an explainable approach to RTE, we propose a novel pipeline that is based on translating text into an Abstract Meaning Representation (AMR) graph. For this we use a pre-trained AMR parser. We then translate the AMR graph into propositional logic and use a SAT solver for automated reasoning. In text, often commonsense suggests that an entailment (or contradiction) relationship holds between a premise and a claim, but because different wordings are used, this is not identified from their logical representations. To address this, we introduce relaxation methods to allow replacement or forgetting of some propositions. Our experimental results show this pipeline performs well on four RTE datasets.

Given the widespread adoption and usage of Large Language Models (LLMs), it is crucial to have flexible and interpretable evaluations of their instruction-following ability. Preference judgments between model outputs have become the de facto evaluation standard, despite distilling complex, multi-faceted preferences into a single ranking. Furthermore, as human annotation is slow and costly, LLMs are increasingly used to make these judgments, at the expense of reliability and interpretability. In this work, we propose TICK (Targeted Instruct-evaluation with ChecKlists), a fully automated, interpretable evaluation protocol that structures evaluations with LLM-generated, instruction-specific checklists. We first show that, given an instruction, LLMs can reliably produce high-quality, tailored evaluation checklists that decompose the instruction into a series of YES/NO questions. Each question asks whether a candidate response meets a specific requirement of the instruction. We demonstrate that using TICK leads to a significant increase (46.4% $\to$ 52.2%) in the frequency of exact agreements between LLM judgements and human preferences, as compared to having an LLM directly score an output. We then show that STICK (Self-TICK) can be used to improve generation quality across multiple benchmarks via self-refinement and Best-of-N selection. STICK self-refinement on LiveBench reasoning tasks leads to an absolute gain of $+$7.8%, whilst Best-of-N selection with STICK attains $+$6.3% absolute improvement on the real-world instruction dataset, WildBench. In light of this, structured, multi-faceted self-improvement is shown to be a promising way to further advance LLM capabilities. Finally, by providing LLM-generated checklists to human evaluators tasked with directly scoring LLM responses to WildBench instructions, we notably increase inter-annotator agreement (0.194 $\to$ 0.256).

RAG 2.0 is really about grounding general purpose agents in proprietary enterprise context. Instead of simply one-shot answering a simple question, the agent…

Vision model based document ingestion.

Colivara is a suite of services that allows you to store, search, and retrieve documents based on their visual embedding. ColiVara has state of the art retrieval performance on both text and visual...

Omni turns documents, slide decks, websites and more into the data you need. You'll never need to copy + paste data into spreadsheets again.

Org profile for SparseCL on Hugging Face, the AI community building the future.