Model: joshuapb/fine-tuned-matryoshka-1000
Author: joshuapb
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:1000, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, en, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:BAAI/bge-base-en-v1.5, base_model:finetune:BAAI/bge-base-en-v1.5, license:apache-2.0, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: BAAI/bge-base-en-v1.5
Card metadata: base_model: BAAI/bge-base-en-v1.5
language: en
library_name: sentence-transformers
license: apache-2.0
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:1000, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: Revision stage: Edit the output to correct content unsupported by evidence while preserving the original content as much as possible. Initialize the revised text $y=x$.

(1) Per $(q_i, e_{ij})$, an agreement model (via few-shot prompting + CoT, $(y, q, e) \to {0,1}$) checks whether the evidence $e_i$ disagrees with the current revised text $y$.
(2) Only if a disagreement is detect, the edit model (via few-shot prompting + CoT, $(y, q, e) \to \text{ new }y$) outputs a new version of $y$ that aims to agree with evidence $e_{ij}$ while otherwise minimally altering $y$.
(3) Finally only a limited number $M=5$ of evidence goes into the attribution report $A$.




Fig. 12. Illustration of RARR (Retrofit Attribution using Research and Revision). (Image source: Gao et al. 2022)
When evaluating the revised text $y$, both attribution and preservation metrics matter.
sentences: What is the impact of claim extraction on the efficiency of query generation within various tool querying methodologies?, What are the implications of integrating both attribution and preservation metrics in the assessment of a revised text for an attribution report?, What impact does the calibration of large language models, as discussed in the research by Kadavath et al. (2022), have on the consistency and accuracy of their responses, particularly in the context of multiple choice questions?, source_sentence: Fig. 1. Knowledge categorization of close-book QA examples based on how likely the model outputs correct answers. (Image source: Gekhman et al. 2024)
Some interesting observations of the experiments, where dev set accuracy is considered a proxy for hallucinations.

Unknown examples are fitted substantially slower than Known.
The best dev performance is obtained when the LLM fits the majority of the Known training examples but only a few of the Unknown ones. The model starts to hallucinate when it learns most of the Unknown examples.
Among Known examples, MaybeKnown cases result in better overall performance, more essential than HighlyKnown ones.
sentences: What are the implications of a language model's performance when it is primarily trained on familiar examples compared to a diverse set of unfamiliar examples, and how does this relate to the phenomenon of hallucinations in language models?, How can the insights gained from the evaluation framework inform the future enhancements of AI models, particularly in terms of improving factual accuracy and entity recognition?, What role does the MPNet model play in evaluating the faithfulness of reasoning paths, particularly in relation to scores of entailment and contradiction?, source_sentence: Non-context LLM: Prompt LLM directly with <atomic-fact> True or False? without additional context.
Retrieval→LLM: Prompt with $k$ related passages retrieved from the knowledge source as context.
Nonparametric probability (NP)): Compute the average likelihood of tokens in the atomic fact by a masked LM and use that to make a prediction.
Retrieval→LLM + NP: Ensemble of two methods.

Some interesting observations on model hallucination behavior:

Error rates are higher for rarer entities in the task of biography generation.
Error rates are higher for facts mentioned later in the generation.
Using retrieval to ground the model generation significantly helps reduce hallucination.
sentences: What methods does the model employ to generate impactful, non-standard verification questions that enhance the fact-checking process?, What impact does the timing of fact presentation in AI outputs have on the likelihood of generating inaccuracies?, What are the benefits of using the 'Factor+revise' strategy in enhancing the reliability of verification processes in few-shot learning, particularly when it comes to identifying inconsistencies?, source_sentence: Research stage: Find related documents as evidence.

(1) First use a query generation model (via few-shot prompting, $x \to {q_1, \dots, q_N}$) to construct a set of search queries ${q_1, \dots, q_N}$ to verify all aspects of each sentence.
(2) Run Google search, $K=5$ results per query $q_i$.
(3) Utilize a pretrained query-document relevance model to assign relevance scores and only retain one most relevant $J=1$ document $e_{i1}, \dots, e_{iJ}$ per query $q_i$.


Revision stage: Edit the output to correct content unsupported by evidence while preserving the original content as much as possible. Initialize the revised text $y=x$.
sentences: In what ways does the process of generating queries facilitate the verification of content accuracy, particularly through the lens of evidence-based editing methodologies?, What role do attribution and preservation metrics play in assessing the quality of revised texts, and how might these factors influence the success of the Evidence Disagreement Detection process?, What are the practical ways to utilize the F1 @ K metric for assessing how well FacTool identifies factual inaccuracies in various fields?, source_sentence: (1) Joint: join with step 2, where the few-shot examples are structured as (response, verification questions, verification answers); The drawback is that the original response is in the context, so the model may repeat similar hallucination.
(2) 2-step: separate the verification planning and execution steps, such as the original response doesn’t impact
(3) Factored: each verification question is answered separately. Say, if a long-form base generation results in multiple verification questions, we would answer each question one-by-one.
(4) Factor+revise: adding a “cross-checking” step after factored verification execution, conditioned on both the baseline response and the verification question and answer. It detects inconsistency.


Final output: Generate the final, refined output. The output gets revised at this step if any inconsistency is discovered.
sentences: What are the key challenges associated with using a pre-training dataset for world knowledge, particularly in maintaining the factual accuracy of the outputs generated by the model?, What obstacles arise when depending on the pre-training dataset in the context of extrinsic hallucination affecting model outputs?, In what ways does the 'Factor+revise' method enhance the reliability of responses when compared to the 'Joint' and '2-step' methods used for cross-checking?
model-index: name: BGE base Financial Matryoshka
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 768
type: dim_768
metrics: type: cosine_accuracy@1
value: 0.8802083333333334
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.984375
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.9947916666666666
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9947916666666666
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8802083333333334
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.328125
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19895833333333335
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09947916666666667
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8802083333333334
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.984375
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.9947916666666666
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9947916666666666
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9495062223081544
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9337673611111109
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.934240845959596
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 512
type: dim_512
metrics: type: cosine_accuracy@1
value: 0.8854166666666666
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.984375
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.9947916666666666
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8854166666666666
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.328125
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19895833333333335
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8854166666666666
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.984375
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.9947916666666666
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9536782535355709
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.937818287037037
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.937818287037037
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 256
type: dim_256
metrics: type: cosine_accuracy@1
value: 0.9010416666666666
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.984375
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.9010416666666666
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.328125
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19999999999999998
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.9010416666666666
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.984375
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9587563670488631
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9446180555555554
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9446180555555556
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 128
type: dim_128
metrics: type: cosine_accuracy@1
value: 0.90625
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.984375
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.90625
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.328125
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19999999999999998
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.90625
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.984375
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9609068566179642
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9474826388888888
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.947482638888889
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 64
type: dim_64
metrics: type: cosine_accuracy@1
value: 0.890625
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.984375
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.890625
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.328125
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19999999999999998
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.890625
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.984375
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9551401340175182
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9396701388888888
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.939670138888889
name: Cosine Map@100
verified: no
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Created: 2024-08-23 23:29:03