uqlm.scorers.longform.baseclass.uncertainty.LongFormUQ#

class uqlm.scorers.longform.baseclass.uncertainty.LongFormUQ(llm=None, scorers=None, granularity='claim', aggregation='mean', claim_decomposition_llm=None, response_refinement=False, claim_filtering_scorer=None, device=None, system_prompt=None, max_calls_per_min=None, use_n_param=False)#

Bases: UncertaintyQuantifier

__init__(llm=None, scorers=None, granularity='claim', aggregation='mean', claim_decomposition_llm=None, response_refinement=False, claim_filtering_scorer=None, device=None, system_prompt=None, max_calls_per_min=None, use_n_param=False)#

Parent class for uncertainty quantification of LLM responses

Parameters:

  • llm (BaseChatModel) – A langchain llm object to get passed to chain constructor. User is responsible for specifying temperature and other relevant parameters to the constructor of their llm object.

  • scorers (List[str], default=None) – Specifies which black box (consistency) scorers to include.

  • aggregation (str, default="mean") – Specifies how to aggregate claim/sentence-level scores to response-level scores. Must be one of ‘min’ or ‘mean’.

  • granularity (str, default="claim") – Specifies whether to decompose and score at claim or sentence level granularity. Must be either “claim” or “sentence”

  • claim_decomposition_llm (langchain BaseChatModel, default=None) – A langchain llm BaseChatModel to be used for decomposing responses into individual claims. Also used for claim refinement. If granularity=”claim” and claim_decomposition_llm is None, the provided llm will be used for claim decomposition.

  • response_refinement (bool, default=False) – Specifies whether to refine responses with uncertainty-aware decoding. This approach removes claims with confidence scores below the response_refinement_threshold and uses the claim_decomposition_llm to reconstruct the response from the retained claims. Only available for claim-level granularity. For more details, refer to Jiang et al., 2024: https://arxiv.org/abs/2410.20783

  • claim_filtering_scorer (Optional[str], default=None) – specifies which scorer to use to filter claims if response_refinement is True. If not provided, defaults to the first element of self.scorers.

  • device (str or torch.device input or torch.device object, default="cpu") – Specifies the device that NLI model use for prediction. Only applies to ‘semantic_negentropy’, ‘noncontradiction’ scorers. Pass a torch.device to leverage GPU.

  • system_prompt (str, default=None) – Optional argument for user to provide custom system prompt. If prompts are list of strings and system_prompt is None, defaults to “You are a helpful assistant.”

  • max_calls_per_min (int, default=None) – Specifies how many api calls to make per minute to avoid a rate limit error. By default, no limit is specified.

  • use_n_param (bool, default=False) – Specifies whether to use n parameter for BaseChatModel. Not compatible with all BaseChatModel classes. If used, it speeds up the generation process substantially when num_responses > 1.

Methods

__init__([llm, scorers, granularity, ...])

Parent class for uncertainty quantification of LLM responses

generate_candidate_responses(prompts[, ...])

This method generates multiple responses for uncertainty estimation.

generate_original_responses(prompts[, ...])

This method generates original responses for uncertainty estimation.

uncertainty_aware_decode(claim_sets, ...[, ...])

async generate_candidate_responses(prompts, num_responses=5, progress_bar=None)#

This method generates multiple responses for uncertainty estimation. If specified in the child class, all responses are postprocessed using the callable function defined by the user.

Return type:

List[List[str]]

Parameters:

  • prompts (List[Union[str, List[BaseMessage]]]) – List of prompts from which LLM responses will be generated. Prompts in list may be strings or lists of BaseMessage. If providing input type List[List[BaseMessage]], refer to https://python.langchain.com/docs/concepts/messages/#langchain-messages for support.

  • num_responses (int, default=5) – The number of sampled responses used to compute consistency.

  • progress_bar (rich.progress.Progress, default=None) – A progress bar object to display progress.

Returns:

A list of sampled responses for each prompt.

Return type:

list of list of str

async generate_original_responses(prompts, top_k_logprobs=None, progress_bar=None)#

This method generates original responses for uncertainty estimation. If specified in the child class, all responses are postprocessed using the callable function defined by the user.

Return type:

List[str]

Parameters:

  • prompts (List[Union[str, List[BaseMessage]]]) – List of prompts from which LLM responses will be generated. Prompts in list may be strings or lists of BaseMessage. If providing input type List[List[BaseMessage]], refer to https://python.langchain.com/docs/concepts/messages/#langchain-messages for support.

  • progress_bar (rich.progress.Progress, default=None) – A progress bar object to display progress.

Returns:

A list of original responses for each prompt.

Return type:

list of str

async uncertainty_aware_decode(claim_sets, claim_scores, response_refinement_threshold=0.3333333333333333, show_progress_bars=True)#
Return type:

List[str]

Parameters:

  • claim_sets (List[List[str]]) – List of original responses decomposed into lists of claims

  • claim_scores (List[List[float]]) – List of lists of claim-level confidence scores to be used for uncertainty-aware filtering

  • response_refinement_threshold (float, default=1/3) – Threshold for uncertainty-aware filtering. Claims with confidence scores below this threshold are dropped from the refined response. Only used if response_refinement is True.

  • progress_bar (rich.progress.Progress, default=None) – If provided, displays a progress bar while scoring responses

References