uqlm.scorers.black_box.BlackBoxUQ#

class uqlm.scorers.black_box.BlackBoxUQ(llm=None, scorers=None, device=None, use_best=True, nli_model_name='microsoft/deberta-large-mnli', postprocessor=None, system_prompt='You are a helpful assistant.', max_calls_per_min=None, sampling_temperature=1.0, use_n_param=False, max_length=2000, verbose=False)#

Bases: UncertaintyQuantifier

__init__(llm=None, scorers=None, device=None, use_best=True, nli_model_name='microsoft/deberta-large-mnli', postprocessor=None, system_prompt='You are a helpful assistant.', max_calls_per_min=None, sampling_temperature=1.0, use_n_param=False, max_length=2000, verbose=False)#

Class for black box uncertainty quantification. Leverages multiple responses to the same prompt to evaluate consistency as an indicator of hallucination likelihood.

Parameters:

  • llm (langchain BaseChatModel, default=None) – A langchain llm BaseChatModel. User is responsible for specifying temperature and other relevant parameters to the constructor of their llm object.

  • scorers (subset of {) – ‘semantic_negentropy’, ‘noncontradiction’, ‘exact_match’, ‘bert_score’, ‘bleurt’, ‘cosine_sim’

  • } – Specifies which black box (consistency) scorers to include. If None, defaults to [“semantic_negentropy”, “noncontradiction”, “exact_match”, “cosine_sim”].

  • default=None – Specifies which black box (consistency) scorers to include. If None, defaults to [“semantic_negentropy”, “noncontradiction”, “exact_match”, “cosine_sim”].

  • 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.

  • use_best (bool, default=True) – Specifies whether to swap the original response for the uncertainty-minimized response based on semantic entropy clusters. Only used if scorers includes ‘semantic_negentropy’ or ‘noncontradiction’.

  • nli_model_name (str, default="microsoft/deberta-large-mnli") – Specifies which NLI model to use. Must be acceptable input to AutoTokenizer.from_pretrained() and AutoModelForSequenceClassification.from_pretrained()

  • postprocessor (callable, default=None) – A user-defined function that takes a string input and returns a string. Used for postprocessing outputs.

  • system_prompt (str or None, default="You are a helpful assistant.") – Optional argument for user to provide custom system prompt

  • 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.

  • sampling_temperature (float, default=1.0) – The ‘temperature’ parameter for llm model to generate sampled LLM responses. Must be greater than 0.

  • 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.

  • max_length (int, default=2000) – Specifies the maximum allowed string length. Responses longer than this value will be truncated to avoid OutOfMemoryError

  • verbose (bool, default=False) – Specifies whether to print the index of response currently being scored.

Methods

__init__([llm, scorers, device, use_best, ...])

Class for black box uncertainty quantification.

generate_and_score(prompts[, num_responses])

Generate LLM responses, sampled LLM (candidate) responses, and compute confidence scores with specified scorers for the provided prompts.

generate_candidate_responses(prompts)

This method generates multiple responses for uncertainty estimation.

generate_original_responses(prompts)

This method generates original responses for uncertainty estimation.

score(responses, sampled_responses)

Compute confidence scores with specified scorers on provided LLM responses.
async generate_and_score(prompts, num_responses=5)#

Generate LLM responses, sampled LLM (candidate) responses, and compute confidence scores with specified scorers for the provided prompts.

Return type:

UQResult

Parameters:

  • prompts (list of str) – A list of input prompts for the model.

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

Returns:

UQResult containing data (prompts, responses, and scores) and metadata

Return type:

UQResult

async generate_candidate_responses(prompts)#

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]]

async generate_original_responses(prompts)#

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]

score(responses, sampled_responses)#

Compute confidence scores with specified scorers on provided LLM responses. Should only be used if responses and sampled responses are already generated. Otherwise, use generate_and_score.

Return type:

UQResult

Parameters:

  • responses (list of str, default=None) – A list of model responses for the prompts.

  • sampled_responses (list of list of str, default=None) – A list of lists of sampled LLM responses for each prompt. These will be used to compute consistency scores by comparing to the corresponding response from responses.

Returns:

UQResult containing data (prompts, responses, and scores) and metadata

Return type:

UQResult

References